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a/ogail-ppo-options-setobs2.py b/ogail-ppo-options-setobs2.py new file mode 100644 index 0000000..8a7f229 --- /dev/null +++ b/ogail-ppo-options-setobs2.py @@ -0,0 +1,174 @@ +# %% +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/requirements.txt b/requirements.txt index 62870bb..0dc31a1 100644 --- a/requirements.txt +++ b/requirements.txt @@ -7,4 +7,6 @@ tqdm ray[tune] hyperopt psutil -fire \ No newline at end of file +fire +stable_baselines3 +tensorboard \ No newline at end of file diff --git a/sgail-ppo-options-setobs2.py b/sgail-ppo-options-setobs2.py index 572a0a2..e21ffdb 100644 --- a/sgail-ppo-options-setobs2.py +++ b/sgail-ppo-options-setobs2.py @@ -6,7 +6,8 @@ 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.optim +import torch + from intersim.envs import IntersimpleLidarFlatRandom from intersim.envs.intersimple import speed_reward import functools @@ -18,27 +19,38 @@ from ray import tune from datetime import datetime import json +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): - DIR = os.path.dirname(os.path.abspath(__file__)) - 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) + np.random.seed(config['seed']) + torch.manual_seed(config['seed']) - 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) - - envs = [SafeOptionsEnv(Setobs( + envs = sum([[SafeOptionsEnv(Setobs( TransformObservation(CollisionPenaltyWrapper(IntersimpleLidarFlatRandom( n_rays=5, reward=functools.partial( @@ -46,15 +58,18 @@ def training_function(config): collision_penalty=0 ), check_collisions=True, - stop_on_collision=config['env']['stop_on_collision'], + stop_on_collision=config['env']['stop_on_collision'], track=track, ), collision_distance=6, collision_penalty=100), lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10)) - ), options=[(0, 5), (1, 5), (2, 5), (4, 5), (6, 5), (8, 5), (10, 5)], - safe_actions_collision_method=config['env']['safe_actions_collision_method'], - abort_unsafe_collision_method=config['env']['abort_unsafe_collision_method']) for _ in range(60)] + ), options=option_list[config['policy']['option']], + safe_actions_collision_method=config['env']['safe_actions_collision_method'], + abort_unsafe_collision_method=config['env']['abort_unsafe_collision_method']) for _ in range(20)] for track in range(4)],[]) env_fn = lambda i: envs[i] - policy = SetMaskedDiscretePolicy(env_fn(0).action_space.n, hidden_layer_size=config['policy']['hidden_layer_size']) # config net architecture + 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']) @@ -77,16 +92,20 @@ def training_function(config): expert_data = (torch.cat(d0), torch.cat(d1), torch.cat(d2), torch.cat(d3)) expert_data = Buffer(*expert_data) - folder = str(datetime.now()) - os.mkdir(os.path.join(DIR, folder)) - with open(os.path.join(DIR, folder, 'config.json'), 'w') as f: + run_folder = str(datetime.now()) + os.mkdir(os.path.join(DIR, run_folder)) + with open(os.path.join(DIR, run_folder, 'config.json'), 'w') as f: json.dump(config, f, indent=4) def callback(info): - tune.report(gen_mean_reward_per_episode=info['gen/mean_reward_per_episode']) - if not info['epoch'] % 10: - torch.save(policy.state_dict(), os.path.join(DIR, folder, f'sgail-ppo-options-setobs2-{info["epoch"]}.pt')) - torch.save(value.state_dict(), os.path.join(DIR, folder, f'sgail-ppo-options-setobs2-value-{info["epoch"]}.pt')) + 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(), os.path.join(DIR, run_folder, f'policy_epoch{ep}.pt')) value, policy = gail_ppo( env_fn=env_fn, @@ -111,6 +130,9 @@ def training_function(config): lr_schedulers=[pi_lr_scheduler], ) + # save model + torch.save(policy.state_dict(), 'policy_final.pt') + analysis = tune.run( training_function, config={ @@ -120,21 +142,25 @@ analysis = tune.run( 'abort_unsafe_collision_method': 'circle', }, 'policy': { - 'learning_rate': tune.grid_search([3e-4]), - 'learning_rate_decay': tune.grid_search([1.0]), - 'clip_ratio': tune.grid_search([0.2]), - 'iterations_per_epoch': tune.grid_search([100]), - 'hidden_layer_size': tune.grid_search([25]) + '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': tune.grid_search([1e-3]), - 'iterations_per_epoch': tune.grid_search([1000]), + 'learning_rate': 1e-3, # tune.grid_search([1e-3]), + 'iterations_per_epoch': 1000, #tune.grid_search([1000]), }, 'discriminator': { - 'learning_rate': tune.grid_search([1e-3]), - 'weight_decay': tune.grid_search([1e-4]), - 'iterations_per_epoch': tune.grid_search([500]), - } + '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, } ) diff --git a/src/core/policy.py b/src/core/policy.py index 96bd5f8..ba4b3f6 100644 --- a/src/core/policy.py +++ b/src/core/policy.py @@ -36,30 +36,31 @@ class BasePolicy(nn.Module): class Policy(BasePolicy): - def __init__(self, *args, **kwargs): + def __init__(self, *args, hidden_layer_size=50, n_hidden_layers=2, activation=nn.Tanh, **kwargs): super().__init__(*args, **kwargs) - self.nn = nn.Sequential( - nn.LazyLinear(50), - nn.Tanh(), - nn.LazyLinear(50), - nn.Tanh(), - nn.LazyLinear(2 * self.action_dim), - ) + layers = sum([[nn.LazyLinear(hidden_layer_size), + activation()] for _ in range(n_hidden_layers)],[]) + self.nn = nn.Sequential(*layers, nn.LazyLinear(2 *self.action_dim)) + + # old + # self.nn = nn.Sequential( + # nn.LazyLinear(50), + # nn.Tanh(), + # nn.LazyLinear(50), + # nn.Tanh(), + # nn.LazyLinear(2 * self.action_dim), + #) def forward(self, states): return self.nn(states) class DiscretePolicy(BasePolicy): - def __init__(self, *args, hidden_layer_size=50, **kwargs): + def __init__(self, *args, hidden_layer_size=50, n_hidden_layers=2, activation=nn.Tanh, **kwargs): super().__init__(*args, **kwargs) - self.nn = nn.Sequential( - nn.LazyLinear(hidden_layer_size), - nn.Tanh(), - nn.LazyLinear(hidden_layer_size), - nn.Tanh(), - nn.LazyLinear(self.action_dim), - ) + layers = sum([[nn.LazyLinear(hidden_layer_size), + activation()] for _ in range(n_hidden_layers)],[]) + self.nn = nn.Sequential(*layers, nn.LazyLinear(self.action_dim)) def forward(self, states): return self.nn(states) diff --git a/src/eval_main.py b/src/eval_main.py index 7fc5885..3f59844 100644 --- a/src/eval_main.py +++ b/src/eval_main.py @@ -37,21 +37,22 @@ def load_policy(method:str, Returns: policy (Optional[BaseAlgorithm]): the policy to evaluate """ + ml = torch.device('cpu') if not torch.cuda.is_available() else None if method == 'idm': policy = IDMRulePolicy(env, **policy_kwargs) elif method == 'bc': policy = SetPolicy(env.action_space.shape[-1]) - policy.load_state_dict(torch.load(policy_file)) + policy.load_state_dict(torch.load(policy_file, map_location=ml)) policy.eval() elif method == 'gail': policy = SetPolicy(env.action_space.shape[-1]) policy(torch.zeros(env.observation_space.shape)) policy = ReparamPolicy(policy) - policy.load_state_dict(torch.load(policy_file)) + policy.load_state_dict(torch.load(policy_file, map_location=ml)) policy.eval() elif method == 'gail-ppo': policy = SetPolicy(env.action_space.shape[-1]) - policy.load_state_dict(torch.load(policy_file)) + policy.load_state_dict(torch.load(policy_file, map_location=ml)) policy.eval() elif method == 'rail': raise NotImplementedError @@ -59,11 +60,11 @@ def load_policy(method:str, policy = SetDiscretePolicy(env.action_space.n) policy(torch.zeros(env.observation_space.shape)) policy = ReparamPolicy(policy) - policy.load_state_dict(torch.load(policy_file)) + policy.load_state_dict(torch.load(policy_file, map_location=ml)) policy.eval() elif method == 'ogail-ppo': policy = SetDiscretePolicy(env.action_space.n) - policy.load_state_dict(torch.load(policy_file)) + policy.load_state_dict(torch.load(policy_file, map_location=ml)) policy.eval() elif method == 'sgail': policy = SetMaskedDiscretePolicy(env.action_space.n) @@ -72,11 +73,11 @@ def load_policy(method:str, torch.zeros(env.observation_space['safe_actions'].shape) ) policy = ReparamSafePolicy(policy) - policy.load_state_dict(torch.load(policy_file)) + policy.load_state_dict(torch.load(policy_file, map_location=ml)) policy.eval() elif method == 'sgail-ppo': policy = SetMaskedDiscretePolicy(env.action_space.n) - policy.load_state_dict(torch.load(policy_file)) + policy.load_state_dict(torch.load(policy_file, map_location=ml)) policy.eval() else: raise NotImplementedError @@ -406,4 +407,4 @@ def eval_main( if __name__=='__main__': import fire - fire.Fire(eval_main) \ No newline at end of file + fire.Fire(eval_main) diff --git a/src/safe_options/options.py b/src/safe_options/options.py index 2038873..662e6bb 100644 --- a/src/safe_options/options.py +++ b/src/safe_options/options.py @@ -52,7 +52,8 @@ def gail(env_fn, expert_data, discriminator, disc_opt, disc_iters, policy, value generator_data.ll.actions += 0.1 * torch.randn_like(generator_data.ll.actions) - logger.add_scalar('gen/mean_episode_length', (~generator_data.ll.dones).sum() / generator_data.ll.states.shape[0], epoch) + gen_mean_episode_length = (~generator_data.ll.dones).sum() / generator_data.ll.states.shape[0] + logger.add_scalar('gen/mean_episode_length', gen_mean_episode_length , epoch) gen_mean_reward_per_episode = generator_data.hl.rewards[~generator_data.hl.dones].sum() / generator_data.hl.states.shape[0] logger.add_scalar('gen/mean_reward_per_episode', gen_mean_reward_per_episode, epoch) logger.add_scalar('gen/unsafe_probability_mass', policy.unsafe_probability_mass(policy(generator_data.hl.states[~generator_data.hl.dones], generator_data.hl.safe_actions[~generator_data.hl.dones])).mean(), epoch) @@ -64,7 +65,8 @@ def gail(env_fn, expert_data, discriminator, disc_opt, disc_iters, policy, value else: generator_data.ll.rewards = -F.logsigmoid(discriminator(generator_data.ll.states, generator_data.ll.actions)) logger.add_scalar('disc/final_loss', loss, epoch) - logger.add_scalar('disc/mean_reward_per_episode', generator_data.ll.rewards[~generator_data.ll.dones].sum() / generator_data.ll.states.shape[0], epoch) + disc_mean_reward_per_episode = generator_data.ll.rewards[~generator_data.ll.dones].sum() / generator_data.ll.states.shape[0] + logger.add_scalar('disc/mean_reward_per_episode', disc_mean_reward_per_episode , epoch) #assert generator_data.ll.rewards.shape == generator_data.ll.dones.shape generator_data.hl.rewards = torch.where(~generator_data.ll.dones, generator_data.ll.rewards, torch.tensor(0.)).sum(-1) @@ -77,7 +79,9 @@ def gail(env_fn, expert_data, discriminator, disc_opt, disc_iters, policy, value 'epoch': epoch, 'value': value, 'policy': policy, - 'gen/mean_reward_per_episode': gen_mean_reward_per_episode, + 'gen/mean_episode_length': gen_mean_episode_length.item(), + 'gen/mean_reward_per_episode': gen_mean_reward_per_episode.item(), + 'disc/mean_reward_per_episode': disc_mean_reward_per_episode.item(), }) return value, policy @@ -94,8 +98,8 @@ def gail_ppo(env_fn, expert_data, discriminator, disc_opt, disc_iters, policy, v generator_data = OptionsRollout(HLBuffer(*hl_data), Buffer(*ll_data)) generator_data.ll.actions += 0.1 * torch.randn_like(generator_data.ll.actions) - - logger.add_scalar('gen/mean_episode_length', (~generator_data.ll.dones).sum() / generator_data.ll.states.shape[0], epoch) + gen_mean_episode_length = (~generator_data.ll.dones).sum() / generator_data.ll.states.shape[0] + logger.add_scalar('gen/mean_episode_length', gen_mean_episode_length, epoch) gen_mean_reward_per_episode = generator_data.hl.rewards[~generator_data.hl.dones].sum() / generator_data.hl.states.shape[0] logger.add_scalar('gen/mean_reward_per_episode', gen_mean_reward_per_episode, epoch) logger.add_scalar('gen/unsafe_probability_mass', policy.unsafe_probability_mass(policy(generator_data.hl.states[~generator_data.hl.dones], generator_data.hl.safe_actions[~generator_data.hl.dones])).mean(), epoch) @@ -107,7 +111,8 @@ def gail_ppo(env_fn, expert_data, discriminator, disc_opt, disc_iters, policy, v else: generator_data.ll.rewards = -F.logsigmoid(discriminator(generator_data.ll.states, generator_data.ll.actions)) logger.add_scalar('disc/final_loss', loss, epoch) - logger.add_scalar('disc/mean_reward_per_episode', generator_data.ll.rewards[~generator_data.ll.dones].sum() / generator_data.ll.states.shape[0], epoch) + disc_mean_reward_per_episode = generator_data.ll.rewards[~generator_data.ll.dones].sum() / generator_data.ll.states.shape[0] + logger.add_scalar('disc/mean_reward_per_episode', disc_mean_reward_per_episode, epoch) #assert generator_data.ll.rewards.shape == generator_data.ll.dones.shape generator_data.hl.rewards = torch.where(~generator_data.ll.dones, generator_data.ll.rewards, torch.tensor(0.)).sum(-1) @@ -120,7 +125,9 @@ def gail_ppo(env_fn, expert_data, discriminator, disc_opt, disc_iters, policy, v 'epoch': epoch, 'value': value, 'policy': policy, - 'gen/mean_reward_per_episode': gen_mean_reward_per_episode, + 'gen/mean_episode_length': gen_mean_episode_length.item(), + 'gen/mean_reward_per_episode': gen_mean_reward_per_episode.item(), + 'disc/mean_reward_per_episode': disc_mean_reward_per_episode.item(), }) for lr_scheduler in lr_schedulers: @@ -248,9 +255,9 @@ class SafeOptionsEnv(OptionsEnv): if d: break - if self.abort_unsafe_collision_method is not None and \ - not feasible(self.env, plan[k:], method=self.abort_unsafe_collision_method): - break + if self.abort_unsafe_collision_method is not None: + if not feasible(self.env, plan[k:], method=self.abort_unsafe_collision_method): + break n_steps = k + 1 return observations, actions, rewards, env_done, plan_done, infos, n_steps