diff --git a/ogail-ppo-options-setobs2.py b/ogail-ppo-options-setobs2.py deleted file mode 100644 index 8a7f229..0000000 --- a/ogail-ppo-options-setobs2.py +++ /dev/null @@ -1,174 +0,0 @@ -# %% -import os - -import gym -from src.safe_options.options import gail_ppo, Buffer -from src.core.value import SetValue -from src.safe_options.policy import SetMaskedDiscretePolicy -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 src.safe_options.options import SafeOptionsEnv -from torch.utils.tensorboard import SummaryWriter -from ray import tune - -DIR = os.path.dirname(os.path.abspath(__file__)) -option_list = [[(vel, time) for vel in [0, 1, 2, 4, 6, 8, 10] for time in [5]], - [(vel, time) for vel in [0, 1, 2, 5, 7.5, 10] for time in [5, 20]], - [(vel, time) for vel in [0, 1, 2, 4, 6, 8, 10] for time in [5, 10, 20]], # was the best in training with single hidden layer, but very slow - [(vel, time) for vel in [0, 1, 2, 5, 7.5, 10] for time in [5, 20, 40]], - [(vel, time) for vel in [0, 2, 5, 10] for time in [5, 10, 20]], - [(vel, time) for vel in [0, 3, 10] for time in [5, 20, 40]] -] - -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']) - - envs = sum([[SafeOptionsEnv(Setobs( - TransformObservation(CollisionPenaltyWrapper(IntersimpleLidarFlatRandom( - n_rays=5, - reward=functools.partial( - speed_reward, - collision_penalty=0 - ), - stop_on_collision=config['stop_on_collision'], track=track, - ), collision_distance=6, collision_penalty=100), lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10)) - ), options=option_list[config['policy']['option']], safe_actions_collision_method=None, - abort_unsafe_collision_method=None) for _ in range(20)] for track in range(4)],[]) - - env_fn = lambda i: envs[i] - - policy = SetMaskedDiscretePolicy(env_fn(0).action_space.n, - n_hidden_layers=config['policy']['n_hidden_layers'], - hidden_layer_size=config['policy']['hidden_layer_size'], - activation=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']) - - discriminator = DeepsetDiscriminator() # config net architecture - disc_opt = torch.optim.Adam(discriminator.parameters(), lr=config['discriminator']['learning_rate'], weight_decay=config['discriminator']['weight_decay']) - - 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']) - - # 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=200, - rollout_episodes=60, - rollout_steps=60, - 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='sgail-ppo-options-setobs2'), - callback=callback, - lr_schedulers=[pi_lr_scheduler], - ) - - # save model - torch.save(policy.state_dict(), 'policy_final.pt') - -analysis = tune.run( - training_function, - config={ - 'stop_on_collision': tune.grid_search([True, False]), - 'policy': { - 'learning_rate': 3e-4, # tune.grid_search([3e-4]), - 'learning_rate_decay': 1.0, #tune.grid_search([1.0]), - 'clip_ratio': 0.2, #tune.grid_search([0.2]), - 'iterations_per_epoch': 100, #tune.grid_search([100]), - 'hidden_layer_size': tune.grid_search([10, 20, 40]), - 'n_hidden_layers': tune.grid_search([2, 3, 4]), - 'activation':tune.grid_search([torch.nn.LeakyReLU, torch.nn.Tanh]), - 'option': tune.grid_search(list(range(len(option_list)))) - }, - 'value': { - 'learning_rate': 1e-3, # tune.grid_search([1e-3]), - 'iterations_per_epoch': 1000, #tune.grid_search([1000]), - }, - 'discriminator': { - 'learning_rate': 1e-3, #tune.grid_search([1e-3]), - 'weight_decay': 1e-4, #tune.grid_search([1e-4]), - 'iterations_per_epoch': 100, #tune.grid_search([100]), - }, - 'seed': 0, - } -) - -print('Best config: ', analysis.get_best_config(metric='gen_mean_reward_per_episode', mode='max')) - -# %% -# policy = SetMaskedDiscretePolicy(env_fn(0).action_space.n) -# policy(torch.zeros(env_fn(0).observation_space['observation'].shape), torch.zeros(env_fn(0).observation_space['safe_actions'].shape)) -# policy.load_state_dict(torch.load('sgail-ppo-options-setobs2.pt')) - -# env = env_fn(0) -# obs = env.reset() -# env.render(mode='post') -# for i in range(300): -# action = policy.sample(policy( -# torch.tensor(obs['observation'], dtype=torch.float32), -# torch.tensor(obs['safe_actions'], dtype=torch.float32), -# )) -# obs, reward, done, _ = env.step(action, render_mode='post') -# print('step', i, 'reward', reward, 'safe actions', obs['safe_actions']) -# if done: -# break -# env.close() -# %% diff --git a/sgail-ppo-options-setobs2.py b/shail-experiment.py similarity index 100% rename from sgail-ppo-options-setobs2.py rename to shail-experiment.py