diff --git 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/src/safe_options/options.py b/src/safe_options/options.py index 75f9e33..3423016 100644 --- a/src/safe_options/options.py +++ b/src/safe_options/options.py @@ -249,9 +249,9 @@ 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