From 37f44605d2b26f4ef64392dcef7440296c7c5473 Mon Sep 17 00:00:00 2001
From: Arec Jamgochian <jamgochian95@gmail.com>
Date: Thu, 3 Mar 2022 19:53:45 -0800
Subject: [PATCH] purging unused files

---
 checkpoints/bc-intersimple-setobs2.pt         | Bin 15383 -> 0 bytes
 .../gail-intersimple-setobs2-03-02-22.pt      | Bin 14187 -> 0 bytes
 .../gail-options-setobs2-15-02-2022.pt        | Bin 15019 -> 0 bytes
 .../gail-options-setobs2-Feb15_18-49-05.pt    | Bin 15019 -> 0 bytes
 checkpoints/gail-ppo-intersimple-setobs2.pt   | Bin 15319 -> 0 bytes
 ...gail-ppo-options-setobs2-Feb15_22-05-38.pt | Bin 16087 -> 0 bytes
 .../sgail-options-setobs2-Feb21_13-30-45.pt   | Bin 15211 -> 0 bytes
 .../sgail-ppo-options-setobs2-17-02-2022.pt   | Bin 16279 -> 0 bytes
 ...gail-ppo-options-setobs2-Feb18_12-53-23.pt | Bin 16279 -> 0 bytes
 ...gail-ppo-options-setobs2-Feb18_16-25-08.pt | Bin 16279 -> 0 bytes
 .../wgail-options-setobs2-Feb16_01-06-27.pt   | Bin 14763 -> 0 bytes
 ...gail-ppo-options-setobs2-Feb16_04-02-56.pt | Bin 15895 -> 0 bytes
 config/networks.json5                         |  42 ---
 config/value_dice.json5                       |  85 -----
 experiments/experiment.py                     | 203 ------------
 experiments/experiments.sh                    |   9 -
 experiments/train_vd.sh                       |   5 -
 src/bc/__init__.py                            |   1 -
 src/bc/bc.py                                  | 191 -----------
 src/data/data_utils.py                        |  96 ------
 src/data/expert_data.py                       | 210 ------------
 src/discriminator/__init__.py                 |   1 -
 src/discriminator/discriminator.py            | 101 ------
 src/discriminator/test_discriminator.py       |  45 ---
 src/main.py                                   | 118 -------
 src/models/__init__.py                        |   0
 src/nets/__init__py                           |   0
 src/nets/deepsets.py                          | 117 -------
 src/nets/util.py                              |  14 -
 src/policies/__init__.py                      |   2 -
 src/policies/options.py                       |  65 ----
 src/policies/policy.py                        | 164 ----------
 src/util/nn_training.py                       |  11 -
 src/util/transform.py                         | 140 --------
 src/value_dice/__init__.py                    |   1 -
 src/value_dice/value_dice.py                  | 300 ------------------
 36 files changed, 1921 deletions(-)
 delete mode 100644 checkpoints/bc-intersimple-setobs2.pt
 delete mode 100644 checkpoints/gail-intersimple-setobs2-03-02-22.pt
 delete mode 100644 checkpoints/gail-options-setobs2-15-02-2022.pt
 delete mode 100644 checkpoints/gail-options-setobs2-Feb15_18-49-05.pt
 delete mode 100644 checkpoints/gail-ppo-intersimple-setobs2.pt
 delete mode 100644 checkpoints/gail-ppo-options-setobs2-Feb15_22-05-38.pt
 delete mode 100644 checkpoints/sgail-options-setobs2-Feb21_13-30-45.pt
 delete mode 100644 checkpoints/sgail-ppo-options-setobs2-17-02-2022.pt
 delete mode 100644 checkpoints/sgail-ppo-options-setobs2-Feb18_12-53-23.pt
 delete mode 100644 checkpoints/sgail-ppo-options-setobs2-Feb18_16-25-08.pt
 delete mode 100644 checkpoints/wgail-options-setobs2-Feb16_01-06-27.pt
 delete mode 100644 checkpoints/wgail-ppo-options-setobs2-Feb16_04-02-56.pt
 delete mode 100644 config/networks.json5
 delete mode 100644 config/value_dice.json5
 delete mode 100644 experiments/experiment.py
 delete mode 100755 experiments/experiments.sh
 delete mode 100755 experiments/train_vd.sh
 delete mode 100644 src/bc/__init__.py
 delete mode 100644 src/bc/bc.py
 delete mode 100644 src/data/data_utils.py
 delete mode 100644 src/data/expert_data.py
 delete mode 100644 src/discriminator/__init__.py
 delete mode 100644 src/discriminator/discriminator.py
 delete mode 100644 src/discriminator/test_discriminator.py
 delete mode 100644 src/main.py
 delete mode 100644 src/models/__init__.py
 delete mode 100644 src/nets/__init__py
 delete mode 100644 src/nets/deepsets.py
 delete mode 100644 src/nets/util.py
 delete mode 100644 src/policies/__init__.py
 delete mode 100644 src/policies/options.py
 delete mode 100644 src/policies/policy.py
 delete mode 100644 src/util/nn_training.py
 delete mode 100644 src/util/transform.py
 delete mode 100644 src/value_dice/__init__.py
 delete mode 100644 src/value_dice/value_dice.py

diff --git a/checkpoints/bc-intersimple-setobs2.pt b/checkpoints/bc-intersimple-setobs2.pt
deleted file mode 100644
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diff --git a/checkpoints/gail-intersimple-setobs2-03-02-22.pt b/checkpoints/gail-intersimple-setobs2-03-02-22.pt
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diff --git a/config/networks.json5 b/config/networks.json5
deleted file mode 100644
index ebce18f..0000000
--- a/config/networks.json5
+++ /dev/null
@@ -1,42 +0,0 @@
-{
-    ego_encoder: {
-        input_dim: 5, // number of state vars
-        hidden_n: 0,
-        hidden_dim: 5,
-        output_dim: 5
-    },
-    deepsets: {
-        input_dim: 6, // number of relative state vars for others
-        phi: {
-            hidden_n: 2,
-            hidden_dim: 20,
-        },
-        latent_dim: 20,
-        rho: {
-            hidden_n: 2,
-            hidden_dim: 10,
-        },
-        output_dim: 10
-    },
-    path_encoder: {
-        input_dim: 40, // 2 * path length for (x,y) coordinates
-        hidden_n: 0,
-        hidden_dim: 20,
-        output_dim: 10,
-    },
-    head: {
-        input_dim: 0, // computed in policy constructor
-        hidden_n: 3,
-        hidden_dim: 50,
-        output_dim: 1, // number of outputs e.g. number of actions, or just one
-        final_activation: 'sigmoid',
-    },
-    optim: {
-            optimizer: 'adam',
-            lr: 1e-3,
-            weight_decay: 0.1,
-    },
-    train_epochs: 200,
-    train_batch_size: 32,
-    loss: 'huber',
-}
\ No newline at end of file
diff --git a/config/value_dice.json5 b/config/value_dice.json5
deleted file mode 100644
index 439eef0..0000000
--- a/config/value_dice.json5
+++ /dev/null
@@ -1,85 +0,0 @@
-{
-    policy_net: {
-        ego_encoder: {
-            input_dim: 5, // number of state vars
-            hidden_n: 0,
-            hidden_dim: 5,
-            output_dim: 5
-        },
-        deepsets: {
-            input_dim: 6, // number of relative state vars for others
-            phi: {
-                hidden_n: 2,
-                hidden_dim: 20,
-            },
-            latent_dim: 20,
-            rho: {
-                hidden_n: 2,
-                hidden_dim: 10,
-            },
-            output_dim: 10
-        },
-        path_encoder: {
-            input_dim: 40, // 2 * path length for (x,y) coordinates
-            hidden_n: 0,
-            hidden_dim: 20,
-            output_dim: 10,
-        },
-        head: {
-            input_dim: 0, // computed in policy constructor
-            hidden_n: 3,
-            hidden_dim: 50,
-            output_dim: 1, // number of outputs e.g. number of actions, or just one
-            final_activation: 'sigmoid',
-        },
-    },
-    value_net: {
-        ego_encoder: {
-            input_dim: 5, // number of state vars
-            hidden_n: 0,
-            hidden_dim: 5,
-            output_dim: 5
-        },
-        deepsets: {
-            input_dim: 6, // number of relative state vars for others
-            phi: {
-                hidden_n: 2,
-                hidden_dim: 20,
-            },
-            latent_dim: 20,
-            rho: {
-                hidden_n: 2,
-                hidden_dim: 10,
-            },
-            output_dim: 10
-        },
-        path_encoder: {
-            input_dim: 40, // 2 * path length for (x,y) coordinates
-            hidden_n: 0,
-            hidden_dim: 20,
-            output_dim: 10,
-        },
-        action_dim: 1, // number of actions
-        head: {
-            input_dim: 0, // computed in policy constructor
-            hidden_n: 3,
-            hidden_dim: 50,
-            output_dim: 1, // number of outputs e.g. number of actions, or just one
-            final_activation: 'id',
-        },
-    },
-    policy_optim: {
-            optimizer: 'adam',
-            lr: 1e-3,
-            weight_decay: 0.1,
-    },
-    value_optim: {
-            optimizer: 'adam',
-            lr: 1e-3,
-            weight_decay: 0.1,
-    },
-    train_epochs: 200,
-    train_batch_size: 32,
-    discount: 0.95,
-    clip_grad_norm: 1.,
-}
\ No newline at end of file
diff --git a/experiments/experiment.py b/experiments/experiment.py
deleted file mode 100644
index bcddb06..0000000
--- a/experiments/experiment.py
+++ /dev/null
@@ -1,203 +0,0 @@
-import json5
-from functools import partial
-import os
-opj = os.path.join
-
-# set up ray tune
-import ray
-from ray import tune
-from ray.tune import Analysis, ExperimentAnalysis
-from ray.tune.schedulers import ASHAScheduler
-from hyperopt import hp
-from ray.tune.suggest.hyperopt import HyperOptSearch
-
-# get graphs
-import intersim
-from intersim.graphs import ConeVisibilityGraph
-
-
-from src.main import basestr, main
-
-def parse_args():
-    """
-    Parse arguments to main
-    Returns:
-        kwargs: dictionary of arguments:
-            train (bool): whether to run train loop
-            test (bool): whether to run test loop
-            method (str): the method to try for imitation
-            loc (int): the location index of the roundabout
-            config (str): config path
-            seed (int): RNG seed
-    """
-    import argparse
-    parser = argparse.ArgumentParser(description='Save Expert Trajectories')
-    parser.add_argument('--loc', default=0, type=int,
-        help='location (default 0)')
-    parser.add_argument("--train", help="train model",
-        action="store_true")
-    parser.add_argument("--ray", help="use ray tune to run multiple experiments",
-        action="store_true")
-    parser.add_argument("--test", help="test model",
-        action="store_true") 
-    parser.add_argument("--method", help="modeling method",
-        choices=['bc', 'gail', 'advil', 'vd'], default='bc')
-    parser.add_argument("--config", help="config file path",
-        default=None, type=str)
-    parser.add_argument('--seed', default=0, type=int,
-        help='seed')    
-    parser.add_argument('--nframes', default=500, type=int,
-        help='frames for test animation') 
-    parser.add_argument('--nsamples', default=200, type=int,
-        help='number of ray samples') 
-    parser.add_argument('--graph', action='store_true',
-        help='whether to mask the relative states based on a ConeVisibilityGraph')
-    parser.add_argument('-d', default='./expert_data', type=str,
-                       help='data directory')
-    parser.add_argument('-o', default=None, type=str,
-                       help='output directory')                   
-    args = parser.parse_args()
-    kwargs = {
-        'train':args.train, 
-        'test':args.test, 
-        'method':args.method,
-        'loc':args.loc,
-        'config_path':args.config,
-        'seed':args.seed,
-        'ray':args.ray,
-        'nframes':args.nframes,
-        'nsamples':args.nsamples,
-        'datadir':os.path.abspath(args.d),
-        'graph':None,
-        'outdir': opj('output',args.method,'loc%02i'%(args.loc)),
-        'train_tracks':[0,1,2],
-        'cv_tracks':[3],
-        'test_tracks':[4],
-    }
-    if args.o:
-        kwargs['outdir'] = args.o
-    if args.graph:
-        kwargs['graph'] = ConeVisibilityGraph(r=20, half_angle=120)
-    return kwargs
-
-def get_full_config(ray_config:dict, method:str)->dict:
-    """
-    Get full model configuration from ray config and method string
-    Args:
-        ray_config (dict): ray config
-        method (str): method to get full configuration for
-    """
-    if method == 'bc':
-        from src.bc import bc_config
-        config = bc_config(ray_config)
-    elif method == 'vd':
-        from src.value_dice import vd_config
-        config = vd_config(ray_config)
-    else:
-        raise NotImplementedError
-    return config
-
-def get_ray_config(method:str)->dict:
-    """
-    Get configuration for ray based on method.
-    Args: 
-        method (str): method to get configuration for
-    Returns:
-        ray_config (dict): configuration for ray
-    """
-    if method == 'bc':
-        ray_config = {
-            "lr": tune.loguniform(1e-5, 1e-3),
-            "weight_decay": tune.choice([0, 0.1]),
-            "loss": tune.choice(['huber', 'mse']),
-            "train_batch_size": tune.choice([16,32,64]),
-            "deepsets_phi_hidden_n": tune.randint(1,5),
-            "deepsets_phi_hidden_dim": tune.lograndint(8,65),
-            "deepsets_latent_dim": tune.lograndint(8,129),
-            "deepsets_rho_hidden_n": tune.randint(0,3),
-            "deepsets_rho_hidden_dim": tune.lograndint(8,129),
-            "deepsets_output_dim": tune.lograndint(4,129),
-            "head_hidden_n": tune.randint(1,6),
-            "head_hidden_dim": tune.lograndint(16,257),
-            "head_final_activation": tune.choice(['sigmoid', None]),
-        }
-    elif method == 'vd':
-        ray_config = {
-            "policy_lr": tune.loguniform(1e-5, 1e-3),
-            "value_lr": tune.loguniform(1e-5, 1e-3),
-            "policy_weight_decay": tune.choice([0, 0.1]),
-            "value_weight_decay": tune.choice([0, 0.1]),
-            "train_batch_size": tune.choice([16,32,64]),
-            "deepsets_phi_hidden_n": tune.randint(1,5),
-            "deepsets_phi_hidden_dim": tune.lograndint(8,65),
-            "deepsets_latent_dim": tune.lograndint(8,129),
-            "deepsets_rho_hidden_n": tune.randint(0,3),
-            "deepsets_rho_hidden_dim": tune.lograndint(8,129),
-            "deepsets_output_dim": tune.lograndint(4,129),
-            "head_hidden_n": tune.randint(1,6),
-            "head_hidden_dim": tune.lograndint(16,257),
-            "head_final_activation": tune.choice(['sigmoid', None]),
-            "clip_grad_norm": tune.choice([.5, 1., 5., 10.]),
-            "discount": tune.choice([.95, .99])
-        }
-    else:
-        raise NotImplementedError
-    return ray_config
-
-if __name__ == '__main__':
-    kwargs = parse_args()
-    
-    # make prefix of output files
-
-    if kwargs['config_path']:
-        # load config
-        with open(kwargs['config_path'], 'r') as cfg:
-            config = json5.load(cfg)
-        if not os.path.isdir(kwargs['outdir']):
-            os.makedirs(kwargs['outdir'])   
-        filestr = opj(kwargs['outdir'], basestr(**kwargs)) 
-        if kwargs['ray']:
-            filestr = kwargs['config_path'].replace('_config.json','')
-        main(config, filestr=filestr, **kwargs)
-
-    elif kwargs['ray'] and kwargs['train']:
-
-        ray.shutdown() 
-        ray.init(log_to_driver=False)
-
-        def ray_train(config, datadir=None):
-            full_config = get_full_config(config, kwargs['method'])
-            main(full_config, filestr='exp', **kwargs)
-        
-        ray_config = get_ray_config(kwargs['method'])
-        search = HyperOptSearch(ray_config, max_concurrent=8, metric='cv_loss',mode="min",)
-        custom_scheduler = ASHAScheduler(metric='cv_loss', mode="min", grace_period=15)
-
-        analysis = tune.run(
-            ray_train, 
-            #config=ray_config,
-            search_alg=search,
-            scheduler=custom_scheduler,
-            local_dir=kwargs['outdir'],
-            #resources_per_trial={"cpu": 2},
-            time_budget_s=120*60,
-            num_samples=kwargs['nsamples'],
-        )
-    elif kwargs['ray'] and kwargs['test']:
-        analysis = Analysis(kwargs['outdir'], default_metric="cv_loss", default_mode="min")
-        config = analysis.get_best_config()
-        filepath = analysis.get_best_logdir()
-        filestr = opj(filepath, 'exp')
-        config_path = filestr+'_config.json'
-        with open(config_path, 'r') as cfg:
-            config = json5.load(cfg)
-        print("Best ray experiment:", filepath)
-        main(config, filestr=filestr, **kwargs)
-    else:
-        raise Exception('No valid config found')
-
-    
-    
-    
-
-    
diff --git a/experiments/experiments.sh b/experiments/experiments.sh
deleted file mode 100755
index 9efc552..0000000
--- a/experiments/experiments.sh
+++ /dev/null
@@ -1,9 +0,0 @@
-#!/bin/sh
-
-python experiments/experiment.py --ray --train -d ./expert_data/base
-python experiments/experiment.py --ray --test -d ./expert_data/base --nframes 1000
-python experiments/experiment.py --ray --train -d ./expert_data/reg
-python experiments/experiment.py --ray --test -d ./expert_data/reg --nframes 1000
-python experiments/experiment.py --ray --train -d ./expert_data/reg_graph --graph
-python experiments/experiment.py --ray --test -d ./expert_data/reg_graph --graph --nframes 1000
-
diff --git a/experiments/train_vd.sh b/experiments/train_vd.sh
deleted file mode 100755
index 13bdc14..0000000
--- a/experiments/train_vd.sh
+++ /dev/null
@@ -1,5 +0,0 @@
-#!/bin/sh
-
-# python experiments/experiment.py --method vd --train --ray -d expert_data/reg -o output/vd/loc00/reg --nsamples 400
-# python experiments/experiment.py --test --ray --method vd -d expert_data/normal -o output/vd/loc00/normal --nframes 1000
-python experiments/experiment.py --train --method vd --config config/value_dice.json5
\ No newline at end of file
diff --git a/src/bc/__init__.py b/src/bc/__init__.py
deleted file mode 100644
index 52cdb6d..0000000
--- a/src/bc/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from src.bc.bc import BehaviorCloningPolicy, train, bc_config
diff --git a/src/bc/bc.py b/src/bc/bc.py
deleted file mode 100644
index ee3b1d7..0000000
--- a/src/bc/bc.py
+++ /dev/null
@@ -1,191 +0,0 @@
-import torch
-import torch.nn as nn
-from torch.utils.data import DataLoader
-import pickle
-from torch.utils.tensorboard import SummaryWriter
-
-from src.policies import IntersimStateNet, IntersimPolicy, generate_transforms
-from src.util.nn_training import optimizer_factory
-from tqdm import tqdm
-import json5
-from ray import tune
-
-def bc_config(ray_config):
-    config = {
-        'ego_encoder': {'input_dim': 5, 'hidden_n': 0, 'hidden_dim':0, 'output_dim': 0},
-        'deepsets': {
-            'input_dim': 6,
-            'phi': {
-                'hidden_n': ray_config['deepsets_phi_hidden_n'],
-                'hidden_dim': ray_config['deepsets_phi_hidden_dim']
-                },
-            'latent_dim': ray_config['deepsets_latent_dim'],
-            'rho': {
-                'hidden_n': ray_config['deepsets_rho_hidden_n'],
-                'hidden_dim': ray_config['deepsets_rho_hidden_dim']
-                },
-            'output_dim': ray_config['deepsets_output_dim']
-        },
-        'path_encoder': {'input_dim': 40, 'hidden_n': 0, 'hidden_dim': 0, 'output_dim': 0},
-        'head': {
-            'input_dim': 0, # computed in constructor
-            'hidden_n': ray_config['head_hidden_n'],
-            'hidden_dim': ray_config['head_hidden_dim'],
-            'output_dim': 1, # number of outputs e.g. number of actions, or just one
-            'final_activation': ray_config['head_final_activation'],
-        },
-        'optim': {
-            'optimizer':'adam',
-            'lr':ray_config['lr'],
-            'weight_decay':ray_config['weight_decay']
-        },
-        'train_epochs': 40,
-        'train_batch_size': ray_config['train_batch_size'],
-        'loss': ray_config['loss'],
-
-    }
-    return config
-
-class BehaviorCloningPolicy(IntersimPolicy):
-    """
-    Class for (continuous) behavior cloning policy
-    """
-    
-    def __init__(self, config: dict, transforms: dict):
-        """
-        Initialize BehaviorCloningPolicy
-        Args:
-            config (dict): configuration file to initialize IntersimDeepSetsNet with
-            transforms (dict): dictionary of transforms to apply to different fields
-        """
-        super(BehaviorCloningPolicy, self).__init__(config, transforms)
-        self._policy = IntersimStateNet(config)
-        
-    @classmethod
-    def load_model(cls, filestr: str, config: dict = None):
-        """
-        Load a model from a file prefix
-        Args:
-            config (dict): configuration dict to set up model
-            filestr (str): string prefix to load model from
-        Returns
-            model (BehaviorCloningPolicy): loaded model
-        """
-        if not config:
-            with open(filestr+'_config.json', 'r') as cfg:
-                config = json5.load(cfg)
-        transforms = pickle.load(open(filestr+'_transforms.pkl', 'rb'))
-        model = cls(config, transforms=transforms)
-        model._policy.load_state_dict(torch.load(filestr+'_model.pt'))
-        return model
-    
-    def eval(self):
-        self._policy.eval()
-
-    def parameters(self):
-        return self._policy.parameters()
-
-    def save_model(self, filestr, save_config=True, save_transforms=True):
-        """
-        Save transforms and state_dict to a location specificed by filestr
-        Args:
-            filestr (str): string prefix to save model to
-            save_config (bool): whether to save the config file (as a json)
-            save_transforms (bool): whether to save transforms (as a pickle)
-        """
-        if save_config:
-            with open(filestr+'_config.json', 'w') as cfg:
-                json5.dump(self._config, cfg)
-        if save_transforms:
-            pickle.dump(self._transforms, open(filestr+'_transforms.pkl', 'wb'))
-        torch.save(self._policy.state_dict(), filestr+'_model.pt')
-
-def train(config, policy, train_dataset, cv_dataset, filestr, **kwargs):
-    
-    using_ray = kwargs.get('ray', False)
-    if using_ray:
-        print('using ray')
-
-    # hyperparams
-    loss_type = config['loss']
-    train_epochs = config['train_epochs']
-    train_batch_size = config['train_batch_size']
-
-    cv_every = 1
-    print_epoch_every = 1000
-    print_cv_every = 5
-    checkpoint_every = 100
-    cv_batch_size = 256 # doesn't matter
-
-    # training and testing dataloaders
-    training_loader = DataLoader(train_dataset, batch_size=train_batch_size, shuffle=True)
-    cv_loader = DataLoader(cv_dataset, batch_size=cv_batch_size, shuffle=True)
-
-    # change policy dtype
-    policy.policy = policy.policy.type(train_dataset[0]['state']['ego_state'].dtype)
-
-    # generate loss function, optimizer
-    cv_loss_fn = nn.MSELoss(reduction='sum')
-    if loss_type == 'huber':
-        loss_fn = nn.HuberLoss(reduction='sum') 
-    elif loss_type == 'mse':
-        loss_fn = nn.MSELoss(reduction='sum')
-    else:
-        raise NotImplementedError
-    optimizer = optimizer_factory(config['optim'], policy.parameters())
-
-    # generate tensorboard writer
-    if not using_ray:
-        writer = SummaryWriter(filestr)
-    
-    for i in tqdm(range(train_epochs)):
-        
-        # save model checkpoints
-        if i % checkpoint_every == 0:
-            policy.save_model(filestr + '_epoch%04i'%(i) )
-
-        # train
-        epoch_loss = 0
-        for (batch_idx, batch) in enumerate(training_loader):
-            
-            # sample mini-batch and run through policy
-            pred_action = policy(batch['state']) 
-            loss = loss_fn(pred_action, batch['action'])
-
-            # compute loss and step optimizer
-            optimizer.zero_grad()
-            loss.backward()
-            optimizer.step()
-
-            epoch_loss += loss.item() / len(train_dataset)
-        
-        # if i % print_epoch_every == 0:
-        #     print('Epoch: {}, Training Loss: {}'.format(i, epoch_loss))
-
-                # measure cv loss
-
-        if i % cv_every == 0:
-            with torch.no_grad():
-                cv_loss = 0.
-                for (batch_idx, batch) in enumerate(cv_loader):
-                    pred_action = policy(batch['state']) 
-                    loss = cv_loss_fn(pred_action, batch['action'])
-                    cv_loss += loss.item() / len(cv_dataset)
-            
-            
-        # Write epoch loss
-        if using_ray:
-            if i % cv_every == 0:
-                tune.report(training_loss=epoch_loss, cv_loss=cv_loss, training_iteration=i+1)
-            else:
-                tune.report(training_loss=epoch_loss, training_iteration=i+1)
-        else:
-            writer.add_scalar('training loss',epoch_loss, i)
-            if i % cv_every == 0:
-                writer.add_scalar('cv loss', cv_loss, i)
-
-        # if i % print_cv_every == 0:    
-        #     print('Epoch: {}, CV Loss: {}'.format(i, cv_loss))
-
-
-    policy.save_model(filestr)
diff --git a/src/data/data_utils.py b/src/data/data_utils.py
deleted file mode 100644
index 0bcf9d4..0000000
--- a/src/data/data_utils.py
+++ /dev/null
@@ -1,96 +0,0 @@
-import torch
-from torch.utils.data import Dataset
-import numpy as np
-from src.data.expert_data import load_expert_data
-import os
-opj = os.path.join
-
-class InteractionDatasetMultiAgent(Dataset):
-    """
-    Class to handle getting full multi-agent observations and actions
-    """
-    pass
-
-class InteractionDatasetSingleAgent(Dataset):
-    """Class to load states and actions for individual agents."""
-
-    def __init__(self, output_dir='expert_data', loc:int = 0, tracks:list = [0], dtype=torch.float32):
-        """
-        Args:
-            output_dir (string): Directory with all the images.
-            loc (int): location index
-            tracks (list[int]): track indices
-        """
-        self.output_dir = output_dir
-        self.loc = loc
-        self.tracks = tracks
-        self.dtype = dtype
-        self.keys = ['ego_state', 'relative_state', 'path', 'action', 'next_ego_state', 'next_relative_state', 'next_path']
-        self._load_dataset()
-
-    def _load_dataset(self):
-        """
-        Load the full datasets ahead of time
-        """
-        self.raw_data = {key:[] for key in self.keys}
-        max_nv = 0
-        for track in self.tracks:
-            try:
-                data = load_expert_data(path=self.output_dir, loc=self.loc, track=track)
-                print('Loaded location {} track {}'.format(self.loc,track))
-            except:
-                print('Failed to load location {} track {}'.format(self.loc,track))
-                continue
-            max_nv = max(max_nv, data['relative_state'].shape[1])
-            for key in self.keys:
-                self.raw_data[key].append(data[key])
-
-        # pad second dimension of relative state
-        for i in range(len(self.raw_data['relative_state'])):
-            nv1, nv2, d = self.raw_data['relative_state'][i].shape
-            pad = torch.zeros(nv1, max_nv-nv2, d, dtype=self.dtype) * np.nan
-            self.raw_data['relative_state'][i] = torch.cat((self.raw_data['relative_state'][i], pad), dim=1)
-            self.raw_data['next_relative_state'][i] = torch.cat((self.raw_data['next_relative_state'][i], pad), dim=1)
-
-        # cat lists
-        for key in self.keys:
-            self.raw_data[key] = torch.cat(self.raw_data[key]).type(self.dtype)
-
-        # mandate equal length
-        lengths = [len(self.raw_data[key]) for key in self.keys]
-        assert min(lengths) == max(lengths), 'dataset lengths unequal'
-        
-    def __len__(self):
-        return len(self.raw_data['ego_state'])
-
-    def __getitem__(self, idx):
-        """ 
-        Sample from the dataset
-        Args:
-            idx: index or indices of B samples
-        Returns:
-            sample (dict): sample dictionary with the following entries:
-                state (dict): state dictionary with the following entries:
-                    ego_state (torch.tensor): (B, 5) raw state
-                    relative_state (torch.tensor): (B, max_nv, d) relative state (padded with nans)
-                    path (torch.tensor): (B, P, 2) tensor of P future path x and y positions
-                action (torch.tensor): (B, 1) actions taken from each state
-                next_stat (dict): next state dictionary with the following entries:
-                    ego_state (torch.tensor): (B, 5) raw next state
-                    relative_state (torch.tensor): (B, max_nv, d) next relative state (padded with nans)
-                    path (torch.tensor): (B, P, 2) tensor of P future next path x and y positions
-        """
-        #sample = {key:self.raw_data[key][idx] for key in self.keys}
-        sample = {
-            'state':{
-                'ego_state':self.raw_data['ego_state'][idx],
-                'relative_state':self.raw_data['relative_state'][idx],
-                'path':self.raw_data['path'][idx]
-            },
-            'action':self.raw_data['action'][idx],
-            'next_state':{
-                'ego_state':self.raw_data['next_ego_state'][idx],
-                'relative_state':self.raw_data['next_relative_state'][idx],
-                'path':self.raw_data['next_path'][idx]},
-        }
-        return sample
\ No newline at end of file
diff --git a/src/data/expert_data.py b/src/data/expert_data.py
deleted file mode 100644
index 4b4b5ba..0000000
--- a/src/data/expert_data.py
+++ /dev/null
@@ -1,210 +0,0 @@
-import torch
-
-import pickle
-import gym
-import numpy as np
-
-import intersim
-from intersim.utils import get_map_path, get_svt, SVT_to_stateactions
-from intersim import collisions
-from intersim.graphs import ConeVisibilityGraph
-import os
-opj = os.path.join
-
-def generate_expert_data(path: str='expert_data', loc: int = 0, track:int = 0, 
-                            mask_relstate: bool = False, regularize_actions: bool = False, 
-                            **kwargs):
-    """
-    Function to save (joint) states and observations from simulated frame
-    Args:
-        path (str): directory to save data
-        loc (int): location index
-        track (int): track index
-        mask_relstate (bool): whether to mask the relative states from the cone visibility graph
-        regularize_actions (bool): whether to regularize the action selection
-        kwargs: arguments for environment instantiation
-    """
-    
-    action_reg = 0.002 if regularize_actions else 0
-    
-    if not os.path.isdir(path):
-        os.makedirs(path)
-    filestr = opj(path,intersim.LOCATIONS[loc]+'_track%03i'%(track))
-    
-    svt, svt_path = get_svt(loc=loc, track=track) #base='InteractionSimulator'
-    osm = get_map_path(loc=loc)
-    print('SVT path: {}'.format(svt_path))
-    print('Map path: {}'.format(osm))
-    states, actions = SVT_to_stateactions(svt)
-
-    # animate from environment
-    if mask_relstate:
-        cvg = ConeVisibilityGraph(r=20, half_angle=120)
-        env = gym.make('intersim:intersim-v0', svt=svt, map_path=osm, 
-            min_acc=-np.inf, max_acc=np.inf, graph=cvg, mask_relstate=True, **kwargs)
-    else:
-        env = gym.make('intersim:intersim-v0', svt=svt, map_path=osm, **kwargs, 
-            min_acc=-np.inf, max_acc=np.inf)
-    
-    env.reset()
-    done = False
-    obs, actions_taken, max_devs = [], [], []
-    i = 0
-    while not done and i < len(actions):
-        # check state deviation
-        env_state = env.projected_state
-        nni = ~torch.isnan(env_state[:,0])
-        norms = torch.norm(env_state[nni,:2]-states[i,nni,:2], dim=1)
-        if len(norms)>0:
-            max_devs.append(norms.max())
-
-        # propagate environment
-        ob, r, done, info = env.step(env.target_state(svt.simstate[i+1], mu=action_reg))
-        obs.append(ob)
-        actions_taken.append(info['action_taken'])
-        i += 1
-    
-    print("Maximum environment deviation from track: %f m" %(max(max_devs)))
-
-    # check for collisions
-    x = torch.stack([ob['state'] for ob in obs])
-    cols = collisions.check_collisions_trajectory(x, svt.lengths, svt.widths)
-    assert ~torch.any(cols), 'Error: Collisions found at indices {}'.format(cols.nonzero(as_tuple=True))
-
-    # shift actions
-    actions_taken.pop(0)
-    obs.pop(-1)
-    actions = torch.stack(actions_taken)
-
-    # save observations and actions
-    pickle.dump(obs,open(filestr+'_raw_observations.pkl', 'wb'))
-    torch.save(actions, filestr+'_raw_actions.pt')
-    process_expert_observations(obs, actions, filestr)
-
-def process_expert_observations(obs, actions, filestr, remove_outliers=True, dtype=torch.float32):
-    """
-    Process the expert observations and save them as torch tensors
-    Args:
-        obs (list[dict]): lost of observations
-        actions (torch.Tensor): (T, nv, a) tensor of actions
-        filestr (str): base filename with which to save out observation tensors
-        remove_outliers (bool): whether to remove datapoints with acceleration above or below 5 m/s/s
-        dtype (torch.Type): type to convert data to
-    """
-    keys = ['ego_state', 'relative_state', 'path', 'action', 'next_ego_state', 'next_relative_state', 'next_path']
-    data = {key:[] for key in keys}
-    assert len(obs) == len(actions), 'non-matching action and observation lengths'
-    T = len(obs)
-    max_nv = 0
-    for t in range(T-1):
-        nni = ~torch.isnan(obs[t]['state'][:,0]) & ~torch.isnan(obs[t+1]['state'][:,0]) 
-        max_nv = max(max_nv,nni.count_nonzero())
-
-        # state
-        data['ego_state'].append(obs[t]['state'][nni])
-        data['relative_state'].append(obs[t]['relative_state'].index_select(0, 
-            nni.nonzero()[:,0]).index_select(1, nni.nonzero()[:,0]))
-        data['path'].append(torch.stack((obs[t]['paths'][0][nni], obs[t]['paths'][1][nni]), dim=-1))
-
-        # action
-        data['action'].append(actions[t][nni])
-
-        # next state
-        data['next_ego_state'].append(obs[t+1]['state'][nni])
-        data['next_relative_state'].append(obs[t+1]['relative_state'].index_select(0, 
-            nni.nonzero()[:,0]).index_select(1, nni.nonzero()[:,0]))
-        data['next_path'].append(torch.stack((obs[t+1]['paths'][0][nni], obs[t+1]['paths'][1][nni]), dim=-1))
-
-    
-
-    # pad second dimension of relative state
-    for i in range(len(data['relative_state'])):
-        nv1, nv2, d = data['relative_state'][i].shape
-        pad = torch.zeros(nv1, max_nv-nv2, d, dtype=dtype) * np.nan
-        data['relative_state'][i] = torch.cat((data['relative_state'][i], pad), dim=1)
-        data['next_relative_state'][i] = torch.cat((data['next_relative_state'][i], pad), dim=1)
-
-    # cat lists 
-    for key in keys:
-        data[key] = torch.cat(data[key]).type(dtype)
-
-    if remove_outliers:
-        non_outlier_indices = torch.nonzero(torch.abs(data['action'][:,0]) < 5)
-        for key in keys:
-            data[key] = data[key][non_outlier_indices[:,0]]    
-
-    # mandate equal length
-    lengths = [len(data[key]) for key in keys]
-    assert min(lengths) == max(lengths), 'dataset lengths unequal'
-
-    # save out data
-    for key in keys:
-        torch.save(data[key], filestr+'_'+key+'.pt')
-
-def load_expert_data(path='expert_data', loc: int = 0, track:int = 0):
-    """
-    Load expert data from processed files.
-    Args:
-        path (str): directory to save data
-        loc (int): location index
-        track (int): track index
-    Returns:
-        data (dict[torch.Tensor]): dict of data
-    """
-    # load observations and actions
-    filestr = opj(path, intersim.LOCATIONS[loc]+'_track%03i'%(track))
-    data = {}
-    keys = ['ego_state', 'relative_state', 'path', 'action', 'next_ego_state', 'next_relative_state', 'next_path']
-    for key in keys:
-        data[key] = torch.load(filestr+'_'+key+'.pt')
-    return data
-
-def load_expert_data_raw(path='expert_data', loc: int = 0, track:int = 0):
-    """
-    Load expert data from raw file.
-    Args:
-        path (str): directory to save data
-        loc (int): location index
-        track (int): track index
-    Returns:
-        obs (list[Observations]): list of observations
-        actions (list[torch.tensor]): list of corresponding actions taken in observations
-    """
-    # load observations and actions
-    filestr = opj(path, intersim.LOCATIONS[loc]+'_track%03i'%(track))
-    obs = pickle.load(open(filestr+'_raw_observations.pkl', 'rb'))
-    actions = torch.load(filestr+'_raw_actions.pt')
-    actions = list(torch.unbind(actions))
-    return obs, actions
-
-if __name__ == '__main__':
-    import argparse
-    parser = argparse.ArgumentParser(description='Save Expert Trajectories')
-    parser.add_argument('--loc', default=0, type=int,
-                       help='location (default 0)')
-    parser.add_argument('--track', default=0, type=int,
-                       help='track number (default 0)')
-    parser.add_argument('--all-tracks', action='store_true',
-                       help='whether to process all tracks at location')
-    parser.add_argument('--graph', action='store_true',
-                       help='whether to mask the relative states based on a ConeVisibilityGraph')
-    parser.add_argument('--reg', action='store_true',
-                       help='whether to regularize actions in the action targeter')
-    parser.add_argument('-o', default='./expert_data', type=str,
-                       help='output folder')
-    args = parser.parse_args()
-
-    kwargs = {
-        'loc':args.loc, 
-        'track': args.track,
-        'path':args.o, 
-        'mask_relstate':args.graph, 
-        'regularize_actions': args.reg
-    }
-
-    if args.all_tracks:
-        for i in range(intersim.MAX_TRACKS):
-            kwargs['track'] = i
-            generate_expert_data(**kwargs)
-    else:
-        generate_expert_data(**kwargs)
\ No newline at end of file
diff --git a/src/discriminator/__init__.py b/src/discriminator/__init__.py
deleted file mode 100644
index 4ad66b2..0000000
--- a/src/discriminator/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from src.discriminator.discriminator import *
\ No newline at end of file
diff --git a/src/discriminator/discriminator.py b/src/discriminator/discriminator.py
deleted file mode 100644
index 1c9664c..0000000
--- a/src/discriminator/discriminator.py
+++ /dev/null
@@ -1,101 +0,0 @@
-import torch
-
-# imitation.rewards.discrim_nets.DiscrimNetGAIL is composed of self.discriminator (nn.Module),
-# which gets called with inputs (state, action) when needed.
-
-class CnnDiscriminator(torch.nn.Module):
-    """ConvNet similar to stable_baselines3.common.policies.ActorCriticCnnPolicy."""
-
-    def __init__(self, env):
-        super().__init__()
-
-        obs_channels, _, _ = env.observation_space.shape
-        (action_size,) = env.action_space.shape
-        in_channels = obs_channels + action_size
-
-        self.cnn = torch.nn.Sequential(
-            torch.nn.Conv2d(in_channels, 32, kernel_size=(8, 8), stride=(4, 4)), # 5+1 -> 32
-            torch.nn.ReLU(),
-            torch.nn.Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2)), # 32 -> 64
-            torch.nn.ReLU(),
-            torch.nn.Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1)), # 64 -> 64
-            torch.nn.ReLU(),
-            torch.nn.Flatten(start_dim=1, end_dim=-1),
-            torch.nn.LazyLinear(512), # 28224 -> 512
-            torch.nn.ReLU(),
-            torch.nn.LazyLinear(1), # 512 -> 1
-        )
-    
-    @staticmethod
-    def _concatenate(state, action):
-        b, _, h, w = state.shape
-        _, a = action.shape
-        act = action.unsqueeze(-1).unsqueeze(-1).expand((b, a, h, w))
-        sa = torch.cat((state, act), -3)
-        return sa
-
-    def forward(self, state, action):
-        sa = self._concatenate(state, action)
-        assert sa.ndim == 4
-        return self.cnn(sa).squeeze(1)
-
-class CnnDiscriminatorFlatAction(torch.nn.Module):
-    """ConvNet similar to stable_baselines3.common.policies.ActorCriticCnnPolicy."""
-
-    def __init__(self, env):
-        super().__init__()
-
-        obs_channels, _, _ = env.observation_space.shape
-        (action_size,) = env.action_space.shape
-        in_channels = obs_channels
-
-        self.cnn = torch.nn.Sequential(
-            torch.nn.Conv2d(in_channels, 32, kernel_size=(8, 8), stride=(4, 4)), # in_channels -> 32
-            torch.nn.ReLU(),
-            torch.nn.Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2)), # 32 -> 64
-            torch.nn.ReLU(),
-            torch.nn.Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1)), # 64 -> 64
-            torch.nn.ReLU(),
-            torch.nn.Flatten(start_dim=1, end_dim=-1),
-            torch.nn.LazyLinear(128), # 28224 -> 128
-        )
-        self.decoder = torch.nn.Sequential(
-            torch.nn.LazyLinear(64), #128 + 2 -> 64
-            torch.nn.ReLU(),
-            torch.nn.LazyLinear(64), #64 -> 64
-            torch.nn.ReLU(),
-            torch.nn.LazyLinear(1) #64 -> 1
-        )
-    
-    @staticmethod
-    def _concatenate(state, action):
-        b, s= state.shape
-        b, a = action.shape
-        sa = torch.cat((state, action), -1)
-        return sa
-
-    def forward(self, state, action):
-        s = self.cnn(state.float())
-        sa = self._concatenate(s, action)
-        assert sa.ndim == 2
-        return self.decoder(sa).squeeze(1)
-
-class MlpDiscriminator(torch.nn.Module):
-    """MLP similar to stable_baselines3.common.policies.ActorCriticPolicy."""
-
-    def __init__(self, env=None):
-        super().__init__()
-        self.flatten = torch.nn.Flatten(start_dim=1, end_dim=-1)
-        self.mlp = torch.nn.Sequential(
-            torch.nn.LazyLinear(64), # 42 -> 64
-            torch.nn.Tanh(),
-            torch.nn.LazyLinear(64), # 64 -> 64
-            torch.nn.Tanh(),
-            torch.nn.LazyLinear(1),  # 64 -> 1
-        )
-    
-    def forward(self, state, action):
-        flat = self.flatten(state)
-        sa = torch.cat((action, flat), -1)
-        assert sa.ndim == 2
-        return self.mlp(sa).squeeze(1)
diff --git a/src/discriminator/test_discriminator.py b/src/discriminator/test_discriminator.py
deleted file mode 100644
index 1de614c..0000000
--- a/src/discriminator/test_discriminator.py
+++ /dev/null
@@ -1,45 +0,0 @@
-from intersim.envs.intersimple import NRasterized
-from discriminator import CnnDiscriminator
-import torch
-
-def test_image_concatenation():
-    env = NRasterized()
-    disc = CnnDiscriminator(env)
-    s = torch.tensor(env.reset()).unsqueeze(0)
-    a = torch.tensor([[0.5]])
-    sa = disc._concatenate(s, a)
-
-    assert s.shape == (1, 5, 200, 200)
-    assert a.shape == (1, 1)
-    assert sa.shape == (1, 6, 200, 200)
-    assert torch.allclose(sa[:, :5], 1.0 * s)
-    assert (sa[:, 5] == a.unsqueeze(-1)).all()
-
-def test_image_concatenation3():
-    env = NRasterized()
-    disc = CnnDiscriminator(env)
-
-    s1 = env.reset()
-    a1 = 0.15
-    s2, _, _, _ = env.step(0.9)
-    a2 = 0.25
-    s3, _, _, _ = env.step(-0.9)
-    a3 = 0.35
-    
-    s = torch.stack([
-        torch.tensor(s1),
-        torch.tensor(s2),
-        torch.tensor(s3)
-    ], axis=0)
-    a = torch.tensor([
-        [a1],
-        [a2],
-        [a3],
-    ])
-    sa = disc._concatenate(s, a)
-
-    assert s.shape == (3, 5, 200, 200)
-    assert a.shape == (3, 1)
-    assert sa.shape == (3, 6, 200, 200)
-    assert torch.allclose(sa[:, :5], 1.0 * s)
-    assert (sa[:, 5] == a.unsqueeze(-1)).all()
diff --git a/src/main.py b/src/main.py
deleted file mode 100644
index edf255c..0000000
--- a/src/main.py
+++ /dev/null
@@ -1,118 +0,0 @@
-import os
-import torch
-import gym
-import intersim
-import numpy as np
-from tqdm import tqdm
-from torch.utils.tensorboard import SummaryWriter
-
-from src import InteractionDatasetSingleAgent, metrics
-from intersim.utils import get_map_path, get_svt
-from src.policies.policy import generate_transforms
-
-def basestr(**kwargs):
-    """
-    Return base prefix for all files relating to a certain experiment
-    Args:
-        kwargs (dict): keyword arguments sent to main training loop
-    Returns:
-        basestr (str): prefix
-    """
-    return 'base'
-
-def main(config, method='bc', train=False, test=False, loc=0, datadir='./expert_data', filestr='', **kwargs):
-    """
-    Main loop for training and testing different imitation models
-    Args:
-        config (dict): configuration dictionary for model
-        train (bool): whether to run train loop
-        test (bool): whether to run test loop
-        method (str): the method to try for imitation
-        loc (int): the location index of the roundabout
-        datadir (str): path to expert data
-        kwargs (dict): remaining kwargs for training loop
-    """
-    # get/set seed
-    seed = kwargs.get('seed',0)
-    torch.manual_seed(seed)
-
-    # method-based training
-    if method=='bc':
-        from src import bc
-        policy_class = bc.BehaviorCloningPolicy
-        train_fn = bc.train
-    elif method=='vd':
-        from src import value_dice
-        policy_class = value_dice.ValueDicePolicy
-        train_fn = value_dice.train
-    else:
-        raise NotImplementedError("Method {} not implemented".format(method))
-    
-    # default train / cv / test split datasets 
-    if train:
-
-        # make policy, train and test datasets, and send to 
-        train_dataset = InteractionDatasetSingleAgent(output_dir=datadir, loc=loc, tracks=kwargs['train_tracks'])
-        # generate transform from train_dataset
-        transforms = generate_transforms(train_dataset)
-        policy = policy_class(config, transforms)
-        cv_dataset = InteractionDatasetSingleAgent(output_dir=datadir, loc=loc, tracks=kwargs['cv_tracks'])
-        train_fn(config, policy, train_dataset, cv_dataset, filestr, **kwargs)
-    
-    if test:
-
-        # load policy 
-        policy = policy_class.load_model(filestr, config)
-        policy.eval()
-
-        # simulate policy
-        simulate_policy(policy, loc=loc, track=kwargs['test_tracks'][0], filestr=filestr, nframes=kwargs['nframes'], graph=kwargs['graph'])
-
-        # run test metrics
-        test_dataset = InteractionDatasetSingleAgent(output_dir=datadir, loc=loc, tracks=kwargs['test_tracks'])
-        writer = SummaryWriter(filestr)
-        info = metrics(filestr, test_dataset, policy)
-        for k, m in info.items():
-            writer.add_scalar('test/{}'.format(k), m, 0)
-
-
-def simulate_policy(policy, loc=0, track=0, filestr='', nframes=float('inf'), graph=None):
-    """
-    Simulate a trained policy
-    Args:
-        policy: the policy to simulate, which should return action directly
-        loc (int): location index to test policy
-        track (int): track to test policy
-        filestr (str): path prefix to save simulation to
-    """
-    # animate from environment
-    basepath = os.path.abspath('./InteractionSimulator')
-    svt, svt_path = get_svt(base=basepath, loc=loc, track=track)
-    osm = get_map_path(base=basepath, loc=loc)
-    if graph:
-        env = gym.make('intersim:intersim-v0', svt=svt, map_path=osm, 
-            min_acc=-np.inf, max_acc=np.inf, graph=graph, mask_relstate=True)
-    else:
-        env = gym.make('intersim:intersim-v0', svt=svt, map_path=osm, 
-            min_acc=-np.inf, max_acc=np.inf)
-    # env = gym.make('intersim:intersim-v0', loc=loc, track=track, 
-    #     min_acc=-np.inf, max_acc=np.inf)
-    
-    ob, _ = env.reset()
-    env.render()
-    done = False
-    i = 0
-    with tqdm(total=min(nframes, env._svt.Tind)) as pbar:
-        while not done and i < nframes:  
-            i += 1
-            
-            # get action
-            action = policy(ob)
-
-            # propagate environment
-            ob, r, done, info = env.step(action)
-            env.render()
-
-            pbar.update()
-
-    env.close(filestr=filestr+'_sim')
diff --git a/src/models/__init__.py b/src/models/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/src/nets/__init__py b/src/nets/__init__py
deleted file mode 100644
index e69de29..0000000
diff --git a/src/nets/deepsets.py b/src/nets/deepsets.py
deleted file mode 100644
index 3b58d16..0000000
--- a/src/nets/deepsets.py
+++ /dev/null
@@ -1,117 +0,0 @@
-import torch
-from torch import nn
-
-from src.nets.util import parse_functional
-
-class DeepSetsModule(nn.Module):
-    def __init__(self, input_dim, phi_hidden_n, phi_hidden_dim, latent_dim, rho_hidden_n, rho_hidden_dim, output_dim):
-        """
-        Args:
-            input_dim (int): input size of one instance of the set; input size of phi
-            phi_hidden_n (int): number of hidden layers in phi
-            phi_hidden_dim (int): size of hidden layers in phi
-            latent_dim (int): output size of phi network, where sum is taken over instances; input size of rho
-            rho_hidden_n (int): number of hidden layers in rho
-            rho_hidden_dim (int): size of hidden layers in rho
-            output_dim (int): output size of rho
-        """
-        super(DeepSetsModule, self).__init__()
-        self.input_dim = input_dim
-        self.latent_dim = latent_dim
-        self.phi = Phi(self.input_dim, phi_hidden_n, phi_hidden_dim, self.latent_dim)
-        self.rho = Phi(self.latent_dim, rho_hidden_n, rho_hidden_dim, output_dim)
-        self.output_dim = self.rho.output_dim
-        self.pooling = torch.sum
-
-    @staticmethod
-    def from_config(config):
-        """
-        Args:
-            config (dict): dictionary with network parameters in the form
-                {
-                    "input_dim": 5,
-                    "phi": {
-                        "hidden_n": 1,
-                        "hidden_dim": 10,
-                    },
-                    "latent_dim": 8,
-                    "rho": {
-                        "hidden_n": 1,
-                        "hidden_dim": 10,
-                    },
-                    "output_dim" : 1,
-                }
-        Returns:
-            m (nn.Module): deep sets module
-        """
-        input_dim = config["input_dim"]
-        phi = config["phi"]
-        latent_dim = config["latent_dim"]
-        rho = config["rho"]
-        output_dim = config["output_dim"]
-        m = DeepSetsModule(input_dim, phi["hidden_n"], phi["hidden_dim"], latent_dim, rho["hidden_n"], rho["hidden_dim"], output_dim)
-        return m
-
-    def forward(self, x):
-        """
-        Args:
-            x (torch.tensor): ([B, ]max_nv, d)
-        Returns:
-            y (torch.tensor): ([B, ]output_dim)
-        """
-        # mask for selecting only those batches and vehicles where all relative states are not nan
-        # shape (B, max_nv)
-        notnan_mask = torch.all(~torch.isnan(x), dim=-1)
-        # create zero tensor of shape (B, max_nv, latent_dim) to store phi evaluations in
-        latent = torch.zeros([*x.shape[:-1], self.latent_dim], dtype=x.dtype)
-        # evaluate phi for all not NaN entries
-        # x[batch_dynamic_mask] has shape (notnan_mask.sum(), input_dim)
-        latent[notnan_mask] = self.phi(x[notnan_mask])
-
-        # sum over relative state dimension
-        latent = self.pooling(latent, dim=-2)
-        
-        # apply rho network
-        y = self.rho(latent)
-        return y
-
-
-class Phi(nn.Module):
-    def __init__(self, input_dim, hidden_n, hidden_dim, output_dim, final_activation=None):
-        """
-        Fully connected feedforward network with same size for all hidden layers and ReLU activation
-
-        Args:
-            input_dim (int): input dimension
-            hidden_n (int): number of hidden layers
-            hidden_dim (int): hidden layer dimension
-            output_dim (int): output dimension
-        """
-        super(Phi, self).__init__()
-        self.input_dim = input_dim
-        self.output_dim = output_dim
-        if hidden_n > 0:
-            self.layers = nn.ModuleList([nn.Linear(self.input_dim, hidden_dim)])
-            for _ in range(hidden_n - 1):
-                self.layers.append(nn.Linear(hidden_dim, hidden_dim))
-            self.layers.append(nn.Linear(hidden_dim, self.output_dim))
-        else:
-            self.layers = nn.ModuleList([nn.Identity()])
-            self.output_dim = self.input_dim
-        self.activation = nn.functional.relu
-        self.final_activation = final_activation if final_activation else lambda x: x
-
-    def forward(self, x):
-        for layer in self.layers[:-1]:
-            x = self.activation(layer(x))
-        x = self.final_activation(self.layers[-1](x))
-        return x
-
-    @staticmethod
-    def from_config(config):
-        args = (config["input_dim"], config["hidden_n"], config["hidden_dim"], config["output_dim"])
-        if "final_activation" in config:
-            kwargs = {"final_activation": parse_functional(config["final_activation"])}
-        else:
-            kwargs = {}
-        return Phi(*args, **kwargs)
diff --git a/src/nets/util.py b/src/nets/util.py
deleted file mode 100644
index 6634224..0000000
--- a/src/nets/util.py
+++ /dev/null
@@ -1,14 +0,0 @@
-import torch
-from torch.nn import functional, Identity
-
-def parse_functional(functional_config):
-    if isinstance(functional_config, str):
-        if functional_config == 'relu':
-            return functional.relu
-        elif functional_config == 'sigmoid':
-            return torch.sigmoid
-        elif functional_config == 'softmax':
-            return functional.softmax
-        elif functional_config == 'id':
-            return Identity()
-    return None
\ No newline at end of file
diff --git a/src/policies/__init__.py b/src/policies/__init__.py
deleted file mode 100644
index 0679815..0000000
--- a/src/policies/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from src.policies.policy import IntersimPolicy, IntersimStateNet, IntersimStateActionNet, generate_transforms
-from src.policies.options import OptionsCnnPolicy
\ No newline at end of file
diff --git a/src/policies/options.py b/src/policies/options.py
deleted file mode 100644
index 2b55c12..0000000
--- a/src/policies/options.py
+++ /dev/null
@@ -1,65 +0,0 @@
-from stable_baselines3.common.policies import ActorCriticPolicy, ActorCriticCnnPolicy
-from torch.distributions import Categorical
-
-class OptionsCnnPolicy(ActorCriticPolicy):
-    """
-    Class for high-level options policy (generator)
-    """
-    def __init__(self, observation_space, *args, eps=0, **kwargs):
-        super().__init__(observation_space, *args, **kwargs)
-        self.cnn_policy = ActorCriticCnnPolicy(observation_space['obs'], *args, **kwargs)
-        self.eps = eps
-
-    def _prior_distribution(self, s):
-        """
-        Return prior distribution over high-level options (before masking)
-        Args:
-            s (torch.tensor): observation
-        Returns:
-            values (torch.tensor): values from critic
-            dist (torch.distributions): prior distribution over actions
-        """
-        latent_pi, latent_vf, latent_sde = self.cnn_policy._get_latent(s)
-        distribution = self.cnn_policy._get_action_dist_from_latent(latent_pi, latent_sde)
-        values = self.cnn_policy.value_net(latent_vf)
-        return values, distribution.distribution
-
-    def forward(self, obs):
-        """
-        Will mask invalid states before making action selections
-        Args:
-            obs: dict with keys:
-                obs (torch.tensor): (*,o)  true observations
-                mask (torch.tensor): (*,m) mask over valid actions
-        Returns:
-            ch (torch.tensor): (*,a) sampled actions
-            values (torch.tensor): (*,) predicted value at observation
-            log_probs (torch.tensor): (*,) log probabilities of selected actions
-        """
-        s, m = obs['obs'], obs['mask']
-        values, prior = self._prior_distribution(s)
-        posterior = Categorical((prior.probs + self.eps) * m)
-        ch = posterior.sample()
-        return ch, values, posterior.log_prob(ch)
-    
-    def _predict(self, obs, deterministic=False):
-        action, _, _ = self.forward(obs)
-        return action
-
-    def evaluate_actions(self, obs, ch):
-        """
-        Evaluate particular actions
-        Args:
-            obs: dict with keys:
-                obs (torch.tensor): (*,o) true observations
-                mask (torch.tensor): (*,m) masks over valid actions
-            ch (torch.tensor): (*,a) selected actions
-        Returns:
-            values (torch.tensor): (*,) predicted value at observation
-            log_probs (torch.tensor): (*,) log probabilities of selected actions
-            ent (torch.tensor): (*,) entropy of each distribution over actions
-        """
-        s, m = obs['obs'], obs['mask']
-        values, prior = self._prior_distribution(s)
-        posterior = Categorical((prior.probs + self.eps) * m)
-        return values, posterior.log_prob(ch), posterior.entropy() # additional values used by PPO.train
diff --git a/src/policies/policy.py b/src/policies/policy.py
deleted file mode 100644
index 274d34b..0000000
--- a/src/policies/policy.py
+++ /dev/null
@@ -1,164 +0,0 @@
-
-import torch
-from torch import nn
-
-from src.nets.deepsets import DeepSetsModule, Phi
-from src.util.transform import MinMaxScaler
-
-class IntersimStateNet(nn.Module):
-    def __init__(self, config):
-        """
-        Args:
-            config (dict): dictionary for configuring the deep sets policy
-        """
-        super(IntersimStateNet, self).__init__()
-        ego_config = config['ego_encoder']
-        deepsets_config = config['deepsets']
-        pathnet_config = config['path_encoder']
-
-        self.ego_net = Phi.from_config(ego_config)
-        self.deepsets_net = DeepSetsModule.from_config(deepsets_config)
-        self.path_net = Phi.from_config(pathnet_config)
-
-        cat_dim = self.ego_net.output_dim + self.deepsets_net.output_dim + self.path_net.output_dim
-        # head has number of concatenated features as input
-        head_config = config['head']
-        head_config["input_dim"] = cat_dim
-        self.head = Phi.from_config(head_config)
-
-    def forward(self, sample):
-        """
-        Args:
-            sample (dict): sample dictionary with the following entries:
-                state (torch.tensor): (B, 5) raw state
-                relative_state (torch.tensor): (B, max_nv, d) relative state (padded with nans)
-                path (torch.tensor): (B, P, 2) tensor of P future path x and y positions
-                action (torch.tensor): (B, 1) actions taken from each state
-        Returns:
-            x (torch.tensor): (head_output_dim,) output of common head network
-        """
-        ego = self.ego_net(sample["ego_state"])
-        relative = self.deepsets_net(sample["relative_state"])
-        path = self.path_net(sample["path"].reshape((sample["path"].shape[0], -1)))
-        x = torch.cat([ego, relative, path], dim=-1)
-        x = self.head(x)
-        return x
-
-
-class IntersimStateActionNet(nn.Module):
-    def __init__(self, config):
-        """
-        Args:
-            config (dict): dictionary for configuring the deep sets policy
-        """
-        super(IntersimStateActionNet, self).__init__()
-        ego_config = config['ego_encoder']
-        deepsets_config = config['deepsets']
-        pathnet_config = config['path_encoder']
-
-        self.ego_net = Phi.from_config(ego_config)
-        self.deepsets_net = DeepSetsModule.from_config(deepsets_config)
-        self.path_net = Phi.from_config(pathnet_config)
-        self.action_dim = config["action_dim"]
-
-        cat_dim = self.ego_net.output_dim + self.deepsets_net.output_dim + self.path_net.output_dim + self.action_dim
-        # head has number of concatenated features as input
-        head_config = config['head']
-        head_config["input_dim"] = cat_dim
-        self.head = Phi.from_config(head_config)
-
-    def forward(self, sample):
-        """
-        Args:
-            sample (dict): sample dictionary with the following entries:
-                state (torch.tensor): (B, 5) raw state
-                relative_state (torch.tensor): (B, max_nv, d) relative state (padded with nans)
-                path_x (torch.tensor): (B, P) tensor of P future path x positions
-                path_y (torch.tensor): (B, P) tensor of P future path y positions
-                action (torch.tensor): (B, 1) actions taken from each state
-        Returns:
-            x (torch.tensor): (head_output_dim,) output of common head network
-        """
-        ego = self.ego_net(sample["ego_state"])
-        relative = self.deepsets_net(sample["relative_state"])
-        action = sample["action"]
-        path = self.path_net(sample["path"].reshape((sample["path"].shape[0], -1)))
-        x = torch.cat([ego, relative, path, action], dim=-1)
-        x = self.head(x)
-        return x
-
-
-class IntersimPolicy():
-    """
-    Base class for intersim policies
-    """
-    def __init__(self, config, transforms):
-        super(IntersimPolicy, self).__init__()
-        self._config = config
-        self._transforms = transforms
-
-    @property
-    def transforms(self):
-        return self._transforms
-    
-    @transforms.setter
-    def transforms(self, transforms):
-        self._transforms=transforms
-
-    @property 
-    def policy(self):
-        return self._policy
-    
-    @policy.setter
-    def policy(self, policy):
-        self._policy = policy
-
-    def transform_observation(self, ob):
-        # run observation through transforms
-        transformed_ob = {}
-        for key in ['ego_state', 'relative_state', 'path', 'action']:             
-            if key in self._transforms.keys() and key in ob.keys():
-                transformed_ob[key] = self._transforms[key].transform(ob[key])
-        return transformed_ob
-
-    def __call__(self, ob):
-
-        if 'ego_state' in ob.keys():
-            # extract state from dataloader samples  
-            pass
-        else:
-            # extract state from observation (using simulator)
-            ob['ego_state'] = ob['state']
-            ob['path'] = torch.stack(ob['paths'],dim=-1)
-
-        ob = self.transform_observation(ob)
-
-        # run transformed state through model
-        action = self._policy(ob)
-        assert action.ndim == 2, 'action has incorrect shape'
-
-        # untransform action
-        if 'action' in self._transforms.keys():
-            action = self._transforms['action'].inverse_transform(action)
-        return action
-
-
-def generate_transforms(dataset):
-    """
-    Generate transform dictionary from dataset
-    Args:
-        dataset (Dataset): dataset of demo observations and actions
-    """
-    transforms = {
-        'action': MinMaxScaler(),
-        'ego_state': MinMaxScaler(),
-        'relative_state': MinMaxScaler(reduce_dim=2),
-        'path': MinMaxScaler(reduce_dim=2),
-    }
-    for key in transforms.keys():
-        if key == 'action':
-            transforms[key].fit(dataset[:][key])
-        else:
-            transforms[key].fit(dataset[:]['state'][key])
-
-    return transforms
diff --git a/src/util/nn_training.py b/src/util/nn_training.py
deleted file mode 100644
index eea6eea..0000000
--- a/src/util/nn_training.py
+++ /dev/null
@@ -1,11 +0,0 @@
-import torch
-
-def optimizer_factory(config, parameters):
-    optimizer_type = config['optimizer']
-    learning_rate = config['lr']
-    weight_decay = config['weight_decay']
-    if optimizer_type == 'adam':  
-        optimizer = torch.optim.Adam(parameters, lr=learning_rate, weight_decay=weight_decay)
-    else:
-        raise NotImplementedError
-    return optimizer
diff --git a/src/util/transform.py b/src/util/transform.py
deleted file mode 100644
index 0b244e9..0000000
--- a/src/util/transform.py
+++ /dev/null
@@ -1,140 +0,0 @@
-import torch
-from torch import nn
-import numpy as np
-from sklearn import preprocessing
-
-class Transform(nn.Module):
-    """
-    Base class to normalize observations and actions for network.
-    """
-
-    def __init__(self):
-        super(Transform, self).__init__()
-        # self.fit(X)
-
-    def fit(self, X):
-        """
-        Fit transformer to X
-        Args:
-            X (torch.tensor): (B, N) tensor of B data points with N features
-        """
-        raise NotImplementedError('Please implement fit()')
-
-    def transform(self, X):
-        """
-        Transform X. fit() has to be called first
-        Args:
-            X (torch.tensor): (B, N) tensor where N has to be the same as during fit()
-        """
-        raise NotImplementedError('Please implement transform()')
-
-    def inverse_transform(self, X):
-        """
-        Inverse transformation
-        Args:
-            X (torch.tensor): (B, N) tensor
-        """
-        raise NotImplementedError('Please implement inverse_transform()')
-
-    def forward(self, X):
-        return self.transform(X)
-
-class MinMaxScaler(Transform):
-    """
-    Scale tensor so each feature is in [0, 1]
-    """
-    def __init__(self, reduce_dim:int=None):
-        """
-        Initialize SciKitTransform
-        Args:
-            reduce_dim (int): dimension to start calculating featues from
-                e.g. with reduce_dim=2, (A, B, C, D, E) will be reshaped to (A*B, C*D*E)
-        """
-        self.reduce_dim = reduce_dim
-        super(MinMaxScaler, self).__init__()
-
-    def fit(self, X):
-        nd = X.ndim
-        if self.reduce_dim:
-            self.nfeatures = int(torch.tensor(X.shape[self.reduce_dim:]).prod())
-        else:
-            assert nd==2, 'Invalid ndim'
-            self.nfeatures = X.shape[1]
-        
-        X = X.reshape((-1,self.nfeatures))
-        nans = torch.isnan(X)
-        X[nans] = float('inf')
-        self.min = X.min(0,keepdims=True)[0]
-
-        X[nans] = -float('inf')
-        self.span = X.max(0,keepdims=True)[0] - self.min
-
-        X[nans] = np.nan
-
-    def transform(self, X):
-        
-        assert hasattr(self, 'min') and hasattr(self, 'span'), 'Model not yet fit'
-        shape = X.shape
-        X = X.reshape((-1,self.nfeatures))
-        t = (X - self.min) / self.span
-        return t.reshape(shape)
-
-    def inverse_transform(self, X):
-
-        assert hasattr(self, 'min') and hasattr(self, 'span'), 'Model not yet fit'
-        shape = X.shape
-        X = X.reshape((-1,self.nfeatures))
-        it =  X * self.span + self.min
-        return it.reshape(shape)
-
-
-class SciKitTransform(Transform):
-    """
-    Wrappers around scikit-learn transforms
-    """
-    def __init__(self, tf, reduce_dim:int=None):
-        """
-        Initialize SciKitTransform
-        Args:
-            tf: transform
-            reduce_dim (int): dimension to start calculating featues from
-                e.g. with reduce_dim=2, (A, B, C, D, E) will be reshaped to (A*B, C*D*E)
-        """
-        self.tf = tf
-        self.reduce_dim = reduce_dim
-        super(SciKitTransform, self).__init__()
-
-    def fit(self, X):
-        nd = X.ndim
-        if self.reduce_dim:
-            self.nfeatures = int(torch.tensor(X.shape[self.reduce_dim:]).prod())
-        else:
-            assert nd==2, 'Invalid ndim'
-            self.nfeatures = X.shape[1]
-
-        self.tf.fit(X.reshape((-1,self.nfeatures)))
-
-    def transform(self, X):
-        shape = X.shape
-        t = torch.tensor(self.tf.transform(X.reshape((-1,self.nfeatures))), dtype=torch.float)
-        return t.reshape(shape)
-
-    def inverse_transform(self, X):
-        shape = X.shape
-        it =  torch.tensor(self.tf.inverse_transform(X.reshape((-1,self.nfeatures))), dtype=torch.float)
-        return it.reshape(shape)
-
-class SciKitStandardScaler(SciKitTransform):
-    """
-    Wrapper around scikit-learn's StandardScaler for standardizing each feature individually.
-    """
-    def __init__(self, **kwargs):
-        super(SciKitStandardScaler, self).__init__(preprocessing.StandardScaler(), **kwargs)
-
-class SciKitMinMaxScaler(SciKitTransform):
-    """
-    Wrapper around scikit-learn's MinMaxScaler for scaling features to [0, 1] individually.
-    """
-    def __init__(self, **kwargs):
-        super(SciKitMinMaxScaler, self).__init__(preprocessing.MinMaxScaler(), **kwargs)
-
diff --git a/src/value_dice/__init__.py b/src/value_dice/__init__.py
deleted file mode 100644
index bb17233..0000000
--- a/src/value_dice/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from src.value_dice.value_dice import ValueDicePolicy, train, vd_config
diff --git a/src/value_dice/value_dice.py b/src/value_dice/value_dice.py
deleted file mode 100644
index d9e29d6..0000000
--- a/src/value_dice/value_dice.py
+++ /dev/null
@@ -1,300 +0,0 @@
-import numpy as np
-
-import torch
-import torch.nn as nn
-from torch.utils.data import DataLoader
-from torch.nn.utils import clip_grad_norm_
-import pickle
-import itertools
-from torch.utils.tensorboard import SummaryWriter
-
-from src.policies import IntersimStateNet, IntersimStateActionNet, IntersimPolicy, generate_transforms
-from src.util.transform import MinMaxScaler
-from src.util.nn_training import optimizer_factory
-from tqdm import tqdm
-import json5
-from ray import tune
-
-def vd_config(ray_config):
-    config = {
-        'policy_net': {
-            'ego_encoder': {'input_dim': 5, 'hidden_n': 0, 'hidden_dim':0, 'output_dim': 0},
-            'deepsets': {
-                'input_dim': 6,
-                'phi': {
-                    'hidden_n': ray_config['deepsets_phi_hidden_n'],
-                    'hidden_dim': ray_config['deepsets_phi_hidden_dim']
-                    },
-                'latent_dim': ray_config['deepsets_latent_dim'],
-                'rho': {
-                    'hidden_n': ray_config['deepsets_rho_hidden_n'],
-                    'hidden_dim': ray_config['deepsets_rho_hidden_dim']
-                    },
-                'output_dim': ray_config['deepsets_output_dim']
-            },
-            'path_encoder': {'input_dim': 40, 'hidden_n': 0, 'hidden_dim': 0, 'output_dim': 0},
-            'head': {
-                'input_dim': 0, # computed in constructor
-                'hidden_n': ray_config['head_hidden_n'],
-                'hidden_dim': ray_config['head_hidden_dim'],
-                'output_dim': 1, # number of outputs e.g. number of actions, or just one
-                'final_activation': ray_config['head_final_activation'],
-            },
-        },
-        'value_net': {
-            'ego_encoder': {'input_dim': 5, 'hidden_n': 0, 'hidden_dim':0, 'output_dim': 0},
-            'deepsets': {
-                'input_dim': 6,
-                'phi': {
-                    'hidden_n': ray_config['deepsets_phi_hidden_n'],
-                    'hidden_dim': ray_config['deepsets_phi_hidden_dim']
-                    },
-                'latent_dim': ray_config['deepsets_latent_dim'],
-                'rho': {
-                    'hidden_n': ray_config['deepsets_rho_hidden_n'],
-                    'hidden_dim': ray_config['deepsets_rho_hidden_dim']
-                    },
-                'output_dim': ray_config['deepsets_output_dim']
-            },
-            'path_encoder': {'input_dim': 40, 'hidden_n': 0, 'hidden_dim': 0, 'output_dim': 0},
-            'action_dim': 1,
-            'head': {
-                'input_dim': 0, # computed in constructor
-                'hidden_n': ray_config['head_hidden_n'],
-                'hidden_dim': ray_config['head_hidden_dim'],
-                'output_dim': 1, # number of outputs e.g. number of actions, or just one
-                'final_activation': ray_config['head_final_activation'],
-            },
-        },
-        'policy_optim': {
-            'optimizer':'adam',
-            'lr':ray_config['policy_lr'],
-            'weight_decay':ray_config['policy_weight_decay']
-        },
-        'value_optim': {
-            'optimizer':'adam',
-            'lr':ray_config['value_lr'],
-            'weight_decay':ray_config['value_weight_decay']
-        },
-        'train_epochs': 40,
-        'train_batch_size': ray_config['train_batch_size'],
-        'discount': ray_config['discount'],
-        'clip_grad_norm': ray_config['clip_grad_norm'],
-    }
-    return config
-
-class ValueDicePolicy(IntersimPolicy):
-    """
-    Class for value dice policy
-    """
-    
-    def __init__(self, config: dict, transforms: dict):
-        """
-        Initialize ValueDicePolicy
-        Args:
-            config (dict): configuration file to initialize IntersimDeepSetsNet with
-            transforms (dict): dictionary of transforms to apply to different fields
-        """
-        super(ValueDicePolicy, self).__init__(config, transforms)
-        self._policy = IntersimStateNet(config['policy_net'])
-        self._value = IntersimStateActionNet(config['value_net'])
-        
-    @property 
-    def value(self):
-        return self._value
-    
-    @value.setter
-    def value(self, value):
-        self._value = value
-
-    @classmethod
-    def load_model(cls, filestr: str, config: dict = None):
-        """
-        Load a model from a file prefix
-        Args:
-            config (dict): configuration dict to set up model
-            filestr (str): string prefix to load model from
-        Returns
-            model (BehaviorCloningPolicy): loaded model
-        """
-        if not config:
-            with open(filestr+'_config.json', 'r') as cfg:
-                config = json5.load(cfg)
-        transforms = pickle.load(open(filestr+'_transforms.pkl', 'rb'))
-        model = cls(config, transforms=transforms)
-        model._policy.load_state_dict(torch.load(filestr+'_policy.pt'))
-        model._value.load_state_dict(torch.load(filestr+'_value.pt'))
-        return model
-    
-    def parameters(self):
-        return itertools.chain(self._policy.parameters(), self._value.parameters())
-
-    def policy_parameters(self):
-        return self.policy.parameters()
-
-    def value_parameters(self):
-        return self.value.parameters()
-
-    def eval(self):
-        self.policy.eval()
-        self.value.eval()
-
-    def save_model(self, filestr, save_config=True, save_transforms=True):
-        """
-        Save transforms and state_dict to a location specificed by filestr
-        Args:
-            filestr (str): string prefix to save model to
-            save_config (bool): whether to save the config file (as a json)
-            save_transforms (bool): whether to save transforms (as a pickle)
-        """
-        if save_config:
-            with open(filestr+'_config.json', 'w') as cfg:
-                json5.dump(self._config, cfg)
-        if save_transforms:
-            pickle.dump(self._transforms, open(filestr+'_transforms.pkl', 'wb'))
-        torch.save(self._policy.state_dict(), filestr+'_policy.pt')
-        torch.save(self._value.state_dict(), filestr+'_value.pt')
-
-
-
-def train(config, policy, train_dataset, cv_dataset, filestr, **kwargs):
-    
-    using_ray = kwargs.get('ray', False)
-    if using_ray:
-        print('using ray')
-
-    # hyperparams
-    train_epochs = config['train_epochs']
-    train_batch_size = config['train_batch_size']
-    discount = config['discount']
-    clip_grad_norm = config['clip_grad_norm']
-
-    cv_every = 1
-    print_epoch_every = 1000
-    print_cv_every = 5
-    checkpoint_every = 100
-    cv_batch_size = 256 # doesn't matter
-
-    # training and testing dataloaders
-    training_loader = DataLoader(train_dataset, batch_size=train_batch_size, shuffle=True)
-    cv_loader = DataLoader(cv_dataset, batch_size=cv_batch_size, shuffle=True)
-
-    # change policy dtype
-    dtype = train_dataset[0]['state']['ego_state'].dtype
-    policy.policy = policy.policy.type(dtype)
-    policy.value = policy.value.type(dtype)
-
-    # define loss function
-    def f_value_dice_loss(batch):
-        # get s, a, s', s_0 from batch
-        state = batch['state']
-        action = batch['action']
-        next_state = batch['next_state']
-        initial_state = state
-
-        # append action to state batches
-        #   use expert action for s
-        state['action'] = action
-        #   run s' and s_0 through policy
-        initial_state['action'] = policy(initial_state)
-        next_state['action'] = policy(next_state)
-
-        # transform state and action before inputting to value network
-        # (for the policy network this is done in policy.__call__() )
-        state = policy.transform_observation(state)
-        initial_state = policy.transform_observation(initial_state)
-        next_state = policy.transform_observation(next_state)
-
-        # evaluate value network
-        value = policy.value(state)
-        value_init = policy.value(initial_state)
-        value_next = policy.value(next_state)
-        
-        # linear loss
-        linear_loss = (1 - discount) * torch.mean(value_init)
-
-        # nonlinear loss
-        value_diff = value - discount * value_next
-        nonlinear_loss = torch.logsumexp(value_diff, dim=0) - np.log(len(value_diff))
-
-        loss = nonlinear_loss - linear_loss
-        return loss
-
-
-    policy_optimizer = optimizer_factory(config['policy_optim'], policy.policy_parameters())
-    value_optimizer = optimizer_factory(config['value_optim'], policy.value_parameters())
-
-    # generate tensorboard writer
-    if not using_ray:
-        writer = SummaryWriter(filestr)
-    
-    for i in tqdm(range(train_epochs)):
-        
-        # save model checkpoints
-        if i % checkpoint_every == 0:
-            policy.save_model(filestr + '_epoch%04i'%(i) )
-
-        # train
-        epoch_loss = 0
-        for (batch_idx, batch) in enumerate(training_loader):
-
-            loss = f_value_dice_loss(batch)
-
-            # In original implementation policy is regularized with orthogonal regularization, 
-            # value with L2 regularization on gradients
-            policy_loss = -loss
-            value_loss = loss
-
-            # # compute loss and step optimizer
-            # policy_optimizer.zero_grad()
-            # value_optimizer.zero_grad()
-            # policy_loss.backward(retain_graph=True)
-            # value_loss.backward()
-
-            # clip_grad_norm_(policy.policy.parameters(), clip_grad_norm)
-            # clip_grad_norm_(policy.value.parameters(), clip_grad_norm)
-
-            # policy_optimizer.step()
-            # value_optimizer.step()
-
-            if batch_idx % 2 == 0:
-                policy_optimizer.zero_grad()
-                policy_loss.backward()
-                clip_grad_norm_(policy.policy.parameters(), clip_grad_norm)
-                policy_optimizer.step()
-            else:
-                value_optimizer.zero_grad()
-                value_loss.backward()
-                clip_grad_norm_(policy.value.parameters(), clip_grad_norm)
-                value_optimizer.step()
-
-            epoch_loss += loss.item() / len(train_dataset)
-        
-        if i % print_epoch_every == 0:
-            print('Epoch: {}, Training Loss: {}'.format(i, epoch_loss))
-
-        # measure cv loss
-        if i % cv_every == 0:
-            with torch.no_grad():
-                cv_loss = 0.
-                for (batch_idx, batch) in enumerate(cv_loader):
-                    loss = f_value_dice_loss(batch)
-                    cv_loss += loss.item() / len(cv_dataset)
-            
-            
-        # Write epoch loss
-        if using_ray:
-            if i % cv_every == 0:
-                tune.report(training_loss=epoch_loss, cv_loss=cv_loss, training_iteration=i+1)
-            else:
-                tune.report(training_loss=epoch_loss, training_iteration=i+1)
-        else:
-            writer.add_scalar('training loss',epoch_loss, i)
-            if i % cv_every == 0:
-                writer.add_scalar('cv loss', cv_loss, i)
-
-        if i % print_cv_every == 0:    
-            print('Epoch: {}, CV Loss: {}'.format(i, cv_loss))
-
-
-    policy.save_model(filestr)