Add tuning script for BC. Todo: if enough GPUs, enable resources in run function
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ebuehrle
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bc-experiment.py
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194
bc-experiment.py
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# %%
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import os
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from tqdm import tqdm
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from src.core.sampling import rollout
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from src.core.gail import gail_ppo, Buffer
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from src.core.value import SetValue
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from src.core.policy import SetPolicy
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from src.core.discriminator import DeepsetDiscriminator
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import torch
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from intersim.envs import IntersimpleLidarFlatRandom
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from intersim.envs.intersimple import speed_reward
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import functools
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from src.util.wrappers import CollisionPenaltyWrapper, TransformObservation, Setobs
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import numpy as np
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from torch.utils.tensorboard import SummaryWriter
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from ray import tune
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from datetime import datetime
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import json
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DIR = os.path.dirname(os.path.abspath(__file__))
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activations = [torch.nn.Tanh, torch.nn.LeakyReLU]
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obs_min = np.array([
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[-1000, -1000, 0, -np.pi, -1e-1, 0.],
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[0, -np.pi, -20, -20, -np.pi, -1e-1],
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[0, -np.pi, -20, -20, -np.pi, -1e-1],
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[0, -np.pi, -20, -20, -np.pi, -1e-1],
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[0, -np.pi, -20, -20, -np.pi, -1e-1],
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[0, -np.pi, -20, -20, -np.pi, -1e-1],
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]).reshape(-1)
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obs_max = np.array([
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[1000, 1000, 20, np.pi, 1e-1, 0.],
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[50, np.pi, 20, 20, np.pi, 1e-1],
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[50, np.pi, 20, 20, np.pi, 1e-1],
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[50, np.pi, 20, 20, np.pi, 1e-1],
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[50, np.pi, 20, 20, np.pi, 1e-1],
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[50, np.pi, 20, 20, np.pi, 1e-1],
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]).reshape(-1)
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def training_function(config):
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np.random.seed(config['seed'])
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torch.manual_seed(config['seed'])
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env = Setobs(TransformObservation(CollisionPenaltyWrapper(
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IntersimpleLidarFlatRandom(
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n_rays=5,
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reward=functools.partial(
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speed_reward,
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collision_penalty=0
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),
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check_collisions=True,
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stop_on_collision=config['trainenv']['stop_on_collision'],
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), collision_distance=6, collision_penalty=100),
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lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10)
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))
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env_fn = lambda i: env
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# load expert data
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if config['experiment'] == 'A':
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expert_data = torch.load(os.path.join(DIR, 'intersimple-expert-data-setobs2-loc0-track0.pt'))
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elif config['experiment'] == 'B':
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expert_data = [
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torch.load(os.path.join(DIR, 'intersimple-expert-data-setobs2-loc0-track0.pt')),
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torch.load(os.path.join(DIR, 'intersimple-expert-data-setobs2-loc0-track1.pt')),
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torch.load(os.path.join(DIR, 'intersimple-expert-data-setobs2-loc0-track2.pt')),
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torch.load(os.path.join(DIR, 'intersimple-expert-data-setobs2-loc0-track3.pt')),
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]
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d0 = [d[0] for d in expert_data]
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d1 = [d[1] for d in expert_data]
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d2 = [d[2] for d in expert_data]
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d3 = [d[3] for d in expert_data]
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expert_data = (torch.cat(d0), torch.cat(d1), torch.cat(d2), torch.cat(d3))
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expert_data = Buffer(*expert_data)
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# configure and train policy
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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policy = SetPolicy(expert_data.actions.shape[-1],
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n_hidden_layers=config['policy']['n_hidden_layers'],
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hidden_layer_size=config['policy']['hidden_layer_size'],
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activation=activations[config['policy']['activation']] ) # config net architecture
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policy = policy.to(device)
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pi_opt = torch.optim.Adam(policy.parameters(), lr=config['policy']['learning_rate'])
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pi_lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(pi_opt, gamma=config['policy']['learning_rate_decay'])
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expert_states = expert_data.states[~expert_data.dones].to(device)
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expert_actions = expert_data.actions[~expert_data.dones].to(device)
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for epoch in range(config['train_epochs']):
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pi_opt.zero_grad()
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loss = -policy.log_prob(policy(expert_states), expert_actions).mean()
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loss.backward()
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pi_opt.step()
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pi_lr_scheduler.step()
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if epoch % 100 == 0:
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gen_states, gen_actions, gen_rewards, gen_dones, gen_collisions = rollout(env_fn, policy.cpu(), n_episodes=60, max_steps_per_episode=200)
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gen_mean_episode_length = (~gen_dones).sum() / gen_states.shape[0]
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gen_mean_reward_per_episode = gen_rewards[~gen_dones].sum() / gen_states.shape[0]
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gen_collision_rate = (1. * gen_collisions.any(-1)).mean()
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tune.report(
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gen_mean_reward_per_episode=gen_mean_reward_per_episode.item(),
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mean_episode_length=gen_mean_episode_length.item(),
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gen_collision_rate=gen_collision_rate.item(),
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loss=loss.item(),
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)
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# save model checkpoints
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ep = epoch + 1
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if (ep % 25 == 0):
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torch.save(policy.state_dict(), f'policy_epoch{ep}.pt')
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# save model
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torch.save(policy.state_dict(), 'policy_final.pt')
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if __name__ == '__main__':
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import argparse
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parser = argparse.ArgumentParser()
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parser.add_argument('--train', choices=['A', 'B'])
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parser.add_argument('--epochs', type=int, default=200)
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parser.add_argument('--test', type=str, help='path to config file to run final training on')
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parser.add_argument('--test_seeds', type=int, default=5)
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parser.add_argument('--test_cpus', type=int, help='number of cpus available to split test seed training over')
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args = parser.parse_args()
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assert (args.train is None) ^ (args.test is None), 'Must either train on an experiment or test with a config file'
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# if no test config specified, train
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if args.test is None:
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print('Running Tuning for Experiment %s'%(args.train))
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analysis = tune.run(
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training_function,
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config={
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'experiment': args.train,
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'trainenv': {
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'stop_on_collision': False,
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},
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'policy': {
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'learning_rate': 3e-4,
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'learning_rate_decay': 1.0,
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'hidden_layer_size': tune.grid_search([20, 40]),
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'n_hidden_layers': tune.grid_search([2, 3]),
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'activation':0,
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},
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'train_epochs': args.epochs,
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'seed': 0,
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}
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# TODO resources_per_trial={'gpu': 1}
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)
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best_config = analysis.get_best_config(metric='gen_collision_rate', mode='min')
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print('Best config: ', best_config)
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# safe best_config
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if not os.path.isdir(os.path.join(DIR, 'best_configs')):
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os.mkdir(os.path.join(DIR, 'best_configs'))
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# save gail
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with open(os.path.join(DIR, 'best_configs',f'bc_exp{args.train}.json'), 'w', encoding='utf-8') as f:
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json.dump(best_config, f, ensure_ascii=False, indent=4)
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# if config file specified, rerun it with appropriate number of seeds
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else:
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with open(args.test, 'rb') as f:
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config = json.load(f)
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print(f'Retraining {args.test} with {args.test_seeds} seeds on experiment {config["experiment"]}')
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# rerun with appropriate number of seeds
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rpt = {'cpu': int(args.test_cpus/args.test_seeds)} if (args.test_cpus is not None) else None
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config['seed'] = tune.grid_search(list(range(1,args.test_seeds+1)))
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analysis = tune.run(training_function, config=config, resources_per_trial=rpt)
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# move final policies to appropriate directory
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split_ = os.path.basename(args.test).split('_')
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model = split_[0]
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exper = split_[-1].split('.')[0]
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savepath = os.path.join('test_policies',model,exper)
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if not os.path.isdir(savepath):
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os.makedirs(savepath)
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import shutil
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for i in range(args.test_seeds):
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s = analysis._checkpoints[i]['config']['seed']
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check_dir = analysis._checkpoints[i]['logdir']
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shutil.copyfile(os.path.join(check_dir,'policy_final.pt'),
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os.path.join(savepath, f'policy_seed{s}.pt'))
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