# %% 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='circle', abort_unsafe_collision_method='circle') 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']) 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=2, # 200 FIXME rollout_episodes=6, #60, FIXME rollout_steps=6, #60, FIXME 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 value, policy 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': 1, #tune.grid_search([1, 2, 3]), #FIXME '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() # %%