# %% import os import gym from src.core.gail import gail_ppo, Buffer from src.core.value import SetValue from src.core.policy import SetPolicy from src.core.discriminator import DeepsetDiscriminator import torch from intersim.envs import IntersimpleLidarFlatRandom from intersim.envs.intersimple import speed_reward import functools from src.util.wrappers import CollisionPenaltyWrapper, TransformObservation, Setobs import numpy as np from torch.utils.tensorboard import SummaryWriter from ray import tune from datetime import datetime import json DIR = os.path.dirname(os.path.abspath(__file__)) activations = [torch.nn.Tanh, torch.nn.LeakyReLU] obs_min = np.array([ [-1000, -1000, 0, -np.pi, -1e-1, 0.], [0, -np.pi, -20, -20, -np.pi, -1e-1], [0, -np.pi, -20, -20, -np.pi, -1e-1], [0, -np.pi, -20, -20, -np.pi, -1e-1], [0, -np.pi, -20, -20, -np.pi, -1e-1], [0, -np.pi, -20, -20, -np.pi, -1e-1], ]).reshape(-1) obs_max = np.array([ [1000, 1000, 20, np.pi, 1e-1, 0.], [50, np.pi, 20, 20, np.pi, 1e-1], [50, np.pi, 20, 20, np.pi, 1e-1], [50, np.pi, 20, 20, np.pi, 1e-1], [50, np.pi, 20, 20, np.pi, 1e-1], [50, np.pi, 20, 20, np.pi, 1e-1], ]).reshape(-1) def training_function(config): np.random.seed(config['seed']) torch.manual_seed(config['seed']) if config['experiment'] == 'A': envs = [Setobs(TransformObservation(CollisionPenaltyWrapper( IntersimpleLidarFlatRandom( n_rays=5, reward=functools.partial( speed_reward, collision_penalty=0 ), check_collisions=True, stop_on_collision=config['trainenv']['stop_on_collision'], ), collision_distance=6, collision_penalty=100), lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10) )) for _ in range(60)] elif config['experiment'] == 'B': envs = sum([[Setobs(TransformObservation(CollisionPenaltyWrapper( IntersimpleLidarFlatRandom( n_rays=5, reward=functools.partial( speed_reward, collision_penalty=0 ), check_collisions=True, stop_on_collision=config['trainenv']['stop_on_collision'], track=track, ), collision_distance=6, collision_penalty=100), lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10) )) for _ in range(15)] for track in range(4)],[]) else: raise NotImplementedError env_fn = lambda i: envs[i] policy = SetPolicy(env_fn(0).action_space.shape[0], n_hidden_layers=config['policy']['n_hidden_layers'], hidden_layer_size=config['policy']['hidden_layer_size'], activation=activations[config['policy']['activation']] ) # config net architecture pi_opt = torch.optim.Adam(policy.parameters(), lr=config['policy']['learning_rate']) pi_lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(pi_opt, gamma=config['policy']['learning_rate_decay']) value = SetValue() # config net architecture v_opt = torch.optim.Adam(value.parameters(), lr=config['value']['learning_rate'], weight_decay=config['value']['weight_decay']) discriminator = DeepsetDiscriminator( n_hidden_layers_element=config['discriminator']['n_hidden_layers_element'], n_hidden_layers_global=config['discriminator']['n_hidden_layers_global'], hidden_layer_size=config['discriminator']['hidden_layer_size'], activation=activations[config['discriminator']['activation']], ) disc_opt = torch.optim.Adam(discriminator.parameters(), lr=config['discriminator']['learning_rate'], weight_decay=config['discriminator']['weight_decay']) if config['experiment'] == 'A': expert_data = torch.load(os.path.join(DIR, 'intersimple-expert-data-setobs2-loc0-track0.pt')) elif config['experiment'] == 'B': expert_data = [ torch.load(os.path.join(DIR, 'intersimple-expert-data-setobs2-loc0-track0.pt')), torch.load(os.path.join(DIR, 'intersimple-expert-data-setobs2-loc0-track1.pt')), torch.load(os.path.join(DIR, 'intersimple-expert-data-setobs2-loc0-track2.pt')), torch.load(os.path.join(DIR, 'intersimple-expert-data-setobs2-loc0-track3.pt')), ] d0 = [d[0] for d in expert_data] d1 = [d[1] for d in expert_data] d2 = [d[2] for d in expert_data] d3 = [d[3] for d in expert_data] expert_data = (torch.cat(d0), torch.cat(d1), torch.cat(d2), torch.cat(d3)) expert_data = Buffer(*expert_data) def callback(info): tune.report(gen_mean_reward_per_episode=info['gen/mean_reward_per_episode'], disc_mean_reward_per_episode=info['disc/mean_reward_per_episode'], mean_episode_length=info['gen/mean_episode_length'], gen_collision_rate=info['gen/collision_rate']) # save model checkpoints ep = info['epoch'] + 1 if (ep % 25 == 0): torch.save(info['policy'].state_dict(), f'policy_epoch{ep}.pt') value, policy = gail_ppo( env_fn=env_fn, expert_data=expert_data, discriminator=discriminator, disc_opt=disc_opt, disc_iters=config['discriminator']['iterations_per_epoch'], policy=policy, value=value, v_opt=v_opt, v_iters=config['value']['iterations_per_epoch'], epochs=config['train_epochs'], rollout_episodes=60, rollout_steps=200, gamma=0.99, gae_lambda=0.9, clip_ratio=config['policy']['clip_ratio'], pi_opt=pi_opt, pi_iters=config['policy']['iterations_per_epoch'], logger=SummaryWriter(comment='gail-ppo-options-setobs2'), callback=callback, lr_schedulers=[pi_lr_scheduler], ) # save model torch.save(policy.state_dict(), 'policy_final.pt') if __name__ == '__main__': import argparse parser = argparse.ArgumentParser() parser.add_argument('--train', choices=['A', 'B']) parser.add_argument('--epochs', type=int, default=200) parser.add_argument('--test', type=str, help='path to config file to run final training on') parser.add_argument('--test_seeds', type=int, default=5) parser.add_argument('--test_cpus', type=int, help='number of cpus available to split test seed training over') args = parser.parse_args() assert (args.train is None) ^ (args.test is None), 'Must either train on an experiment or test with a config file' # if no test config specified, train if args.test is None: print('Running Tuning for Experiment %s'%(args.train)) analysis = tune.run( training_function, config={ 'experiment': args.train, 'trainenv': { 'stop_on_collision': False, }, 'policy': { 'learning_rate': 3e-4, 'learning_rate_decay': 1.0, 'clip_ratio': 0.2, 'iterations_per_epoch': 100, 'hidden_layer_size': tune.grid_search([20, 40]), 'n_hidden_layers': tune.grid_search([2, 3]), 'activation':0, }, 'value': { 'learning_rate': 1e-4, 'weight_decay': 1e-3, 'iterations_per_epoch': 1000, }, 'discriminator': { 'learning_rate': 1e-3, 'weight_decay': 1e-4, 'iterations_per_epoch': 100, 'n_hidden_layers_element': tune.grid_search([3,4]), 'n_hidden_layers_global': tune.grid_search([1,2]), 'hidden_layer_size': 10, 'activation': 0, }, 'train_epochs': args.epochs, 'seed': 0, } ) best_config = analysis.get_best_config(metric='gen_collision_rate', mode='min') print('Best config: ', best_config) # safe best_config if not os.path.isdir(os.path.join(DIR, 'best_configs')): os.mkdir(os.path.join(DIR, 'best_configs')) # save gail with open(os.path.join(DIR, 'best_configs',f'gail_exp{args.train}.json'), 'w', encoding='utf-8') as f: json.dump(best_config, f, ensure_ascii=False, indent=4) # if config file specified, rerun it with appropriate number of seeds else: with open(args.test, 'rb') as f: config = json.load(f) print(f'Retraining {args.test} with {args.test_seeds} seeds on experiment {config["experiment"]}') # rerun with appropriate number of seeds rpt = {'cpu': int(args.test_cpus/args.test_seeds)} if (args.test_cpus is not None) else None config['seed'] = tune.grid_search(list(range(1,args.test_seeds+1))) analysis = tune.run(training_function, config=config, resources_per_trial=rpt) # move final policies to appropriate directory split_ = os.path.basename(args.test).split('_') model = split_[0] exper = split_[-1].split('.')[0] savepath = os.path.join('test_policies',model,exper) if not os.path.isdir(savepath): os.makedirs(savepath) import shutil for i in range(args.test_seeds): s = analysis._checkpoints[i]['config']['seed'] check_dir = analysis._checkpoints[i]['logdir'] shutil.copyfile(os.path.join(check_dir,'policy_final.pt'), os.path.join(savepath, f'policy_seed{s}.pt'))