# %% import os from tqdm import tqdm from src.core.sampling import rollout 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']) env = 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) )) env_fn = lambda i: env # load expert data 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) # configure and train policy device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') policy = SetPolicy(expert_data.actions.shape[-1], n_hidden_layers=config['policy']['n_hidden_layers'], hidden_layer_size=config['policy']['hidden_layer_size'], activation=activations[config['policy']['activation']] ) # config net architecture policy = policy.to(device) 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']) expert_states = expert_data.states[~expert_data.dones].to(device) expert_actions = expert_data.actions[~expert_data.dones].to(device) for epoch in range(config['train_epochs']): pi_opt.zero_grad() loss = -policy.log_prob(policy(expert_states), expert_actions).mean() loss.backward() pi_opt.step() pi_lr_scheduler.step() if epoch % 100 == 0: gen_states, gen_actions, gen_rewards, gen_dones, gen_collisions = rollout(env_fn, policy.cpu(), n_episodes=60, max_steps_per_episode=200) gen_mean_episode_length = (~gen_dones).sum() / gen_states.shape[0] gen_mean_reward_per_episode = gen_rewards[~gen_dones].sum() / gen_states.shape[0] gen_collision_rate = (1. * gen_collisions.any(-1)).mean() tune.report( gen_mean_reward_per_episode=gen_mean_reward_per_episode.item(), mean_episode_length=gen_mean_episode_length.item(), gen_collision_rate=gen_collision_rate.item(), loss=loss.item(), ) # save model checkpoints ep = epoch + 1 if (ep % 25 == 0): torch.save(policy.state_dict(), f'policy_epoch{ep}.pt') # 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, 'hidden_layer_size': tune.grid_search([20, 40]), 'n_hidden_layers': tune.grid_search([2, 3]), 'activation':0, }, 'train_epochs': args.epochs, 'seed': 0, } # TODO resources_per_trial={'gpu': 1} ) 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'bc_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'))