New trained model
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21cefbab48
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347
DPI/train.py
347
DPI/train.py
@ -44,39 +44,41 @@ def parse_args():
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parser.add_argument('--high_noise', action='store_true')
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# replay buffer
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parser.add_argument('--replay_buffer_capacity', default=50000, type=int) #50000
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parser.add_argument('--episode_length', default=51, type=int)
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parser.add_argument('--episode_length', default=21, type=int)
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# train
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parser.add_argument('--agent', default='dpi', type=str, choices=['baseline', 'bisim', 'deepmdp', 'db', 'dpi', 'rpc'])
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parser.add_argument('--init_steps', default=10000, type=int)
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parser.add_argument('--num_train_steps', default=100000, type=int)
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parser.add_argument('--batch_size', default=50, type=int) #512
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parser.add_argument('--state_size', default=512, type=int)
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parser.add_argument('--batch_size', default=128, type=int) #512
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parser.add_argument('--state_size', default=30, type=int)
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parser.add_argument('--hidden_size', default=256, type=int)
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parser.add_argument('--history_size', default=256, type=int)
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parser.add_argument('--history_size', default=128, type=int)
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parser.add_argument('--episode_collection', default=5, type=int)
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parser.add_argument('--episodes_buffer', default=20, type=int)
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parser.add_argument('--num-units', type=int, default=50, help='num hidden units for reward/value/discount models')
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parser.add_argument('--load_encoder', default=None, type=str)
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parser.add_argument('--imagine_horizon', default=15, type=str)
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parser.add_argument('--imagine_horizon', default=10, type=str)
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parser.add_argument('--grad_clip_norm', type=float, default=100.0, help='Gradient clipping norm')
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# eval
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parser.add_argument('--eval_freq', default=10, type=int) # TODO: master had 10000
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parser.add_argument('--num_eval_episodes', default=20, type=int)
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parser.add_argument('--evaluation_interval', default=10000, type=int) # TODO: master had 10000
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# value
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parser.add_argument('--value_lr', default=1e-3, type=float)
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parser.add_argument('--value_lr', default=8e-6, type=float)
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parser.add_argument('--value_beta', default=0.9, type=float)
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parser.add_argument('--value_tau', default=0.005, type=float)
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parser.add_argument('--value_target_update_freq', default=100, type=int)
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parser.add_argument('--td_lambda', default=0.95, type=int)
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# actor
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parser.add_argument('--actor_lr', default=1e-3, type=float)
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parser.add_argument('--actor_lr', default=8e-6, type=float)
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parser.add_argument('--actor_beta', default=0.9, type=float)
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parser.add_argument('--actor_log_std_min', default=-10, type=float)
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parser.add_argument('--actor_log_std_max', default=2, type=float)
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parser.add_argument('--actor_update_freq', default=2, type=int)
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# world/encoder/decoder
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parser.add_argument('--encoder_type', default='pixel', type=str, choices=['pixel', 'pixelCarla096', 'pixelCarla098', 'identity'])
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parser.add_argument('--world_model_lr', default=1e-3, type=float)
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parser.add_argument('--encoder_tau', default=0.001, type=float)
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parser.add_argument('--world_model_lr', default=1e-5, type=float)
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parser.add_argument('--encoder_tau', default=0.005, type=float)
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parser.add_argument('--decoder_type', default='pixel', type=str, choices=['pixel', 'identity', 'contrastive', 'reward', 'inverse', 'reconstruction'])
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parser.add_argument('--num_layers', default=4, type=int)
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parser.add_argument('--num_filters', default=32, type=int)
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@ -117,6 +119,7 @@ class DPI:
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self.env = make_env(self.args)
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#self.args.seed = np.random.randint(0, 1000)
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self.env.seed(self.args.seed)
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self.global_episodes = 0
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# noiseless environment setup
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self.args.version = 2 # env_id changes to v2
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@ -160,7 +163,7 @@ class DPI:
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self.obs_decoder = ObservationDecoder(
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state_size=self.args.state_size, # 128
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output_shape=(self.args.channels,self.args.image_size,self.args.image_size) # (3,84,84)
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output_shape=(self.args.channels*self.args.channels,self.args.image_size,self.args.image_size) # (3,84,84)
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).to(device)
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self.transition_model = TransitionModel(
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@ -176,7 +179,9 @@ class DPI:
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hidden_size=self.args.hidden_size, # 256,
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action_size=self.env.action_space.shape[0], # 6
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).to(device)
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self.actor_model.apply(self.init_weights)
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# Value Models
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self.value_model = ValueModel(
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state_size=self.args.state_size, # 128
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@ -209,23 +214,33 @@ class DPI:
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self.contrastive_head = ContrastiveHead(
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hidden_size=self.args.hidden_size, # 256
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).to(device)
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self.club_sample = CLUBSample(
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x_dim=self.args.state_size, # 128
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y_dim=self.args.state_size, # 128
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hidden_size=self.args.hidden_size, # 256
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).to(device)
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# model parameters
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self.world_model_parameters = list(self.obs_encoder.parameters()) + list(self.prjoection_head.parameters()) + \
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list(self.transition_model.parameters()) + list(self.obs_decoder.parameters()) + \
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list(self.reward_model.parameters())
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list(self.reward_model.parameters()) + list(self.club_sample.parameters())
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self.past_transition_parameters = self.transition_model.parameters()
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# optimizers
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self.world_model_opt = torch.optim.Adam(self.world_model_parameters, self.args.world_model_lr)
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self.value_opt = torch.optim.Adam(self.value_model.parameters(), self.args.value_lr)
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self.actor_opt = torch.optim.Adam(self.actor_model.parameters(), self.args.actor_lr)
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#self.reward_opt = torch.optim.Adam(self.reward_model.parameters(), 1e-5)
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#self.decoder_opt = torch.optim.Adam(self.obs_decoder.parameters(), 1e-4)
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# Create Modules
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self.world_model_modules = [self.obs_encoder, self.prjoection_head, self.transition_model, self.obs_decoder, self.reward_model]
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self.world_model_modules = [self.obs_encoder, self.prjoection_head, self.transition_model, self.obs_decoder, self.reward_model, self.club_sample]
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self.value_modules = [self.value_model]
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self.actor_modules = [self.actor_model]
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#self.reward_modules = [self.reward_model]
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#self.decoder_modules = [self.obs_decoder]
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if use_saved:
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self._use_saved_models(saved_model_dir)
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@ -240,79 +255,98 @@ class DPI:
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done = False
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all_rews = []
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#video = VideoRecorder(self.video_dir if args.save_video else None, resource_files=args.resource_files)
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for episode_count in tqdm.tqdm(range(episodes), desc='Collecting episodes'):
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#if args.save_video:
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# self.env.video.init(enabled=True)
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self.global_episodes += 1
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epi_reward = 0
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for i in range(self.args.episode_length):
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while not done:
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if random:
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action = self.env.action_space.sample()
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else:
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with torch.no_grad():
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obs_torch = torch.unsqueeze(torch.tensor(obs).float(),0).to(device)
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state = self.obs_encoder(obs_torch)["distribution"].sample()
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action = self.actor_model(state).cpu().detach().numpy().squeeze()
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obs = torch.tensor(obs.copy(), dtype=torch.float32).unsqueeze(0)
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obs_processed = preprocess_obs(obs).to(device)
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state = self.obs_encoder(obs_processed)["distribution"].sample()
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action = self.actor_model(state).cpu().numpy().squeeze()
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#action = self.env.action_space.sample()
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next_obs, rew, done, _ = self.env.step(action)
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self.data_buffer.add(obs, action, next_obs, rew, episode_count+1, done)
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#if args.save_video:
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# self.env.video.record(self.env)
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if done: #or i == self.args.episode_length-1:
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obs = self.env.reset()
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done=False
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else:
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obs = next_obs
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self.data_buffer.add(obs, action, next_obs, rew, done, self.global_episodes)
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obs = next_obs
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epi_reward += rew
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obs = self.env.reset()
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done=False
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all_rews.append(epi_reward)
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#if args.save_video:
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# self.env.video.save('noisy/%d.mp4' % episode_count)
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#print("Collected {} random episodes".format(episode_count+1))
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return all_rews
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def train(self, step, total_steps):
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counter = 0
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import matplotlib.pyplot as plt
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fig, ax = plt.subplots()
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while step < total_steps:
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# collect experience
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if step !=0:
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encoder = self.obs_encoder
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actor = self.actor_model
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all_rews = self.collect_sequences(10, random=False, actor_model=actor, encoder_model=encoder)
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all_rews = self.collect_sequences(self.args.episode_collection, random=False, actor_model=actor, encoder_model=encoder)
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else:
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all_rews = self.collect_sequences(self.args.batch_size, random=True)
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all_rews = self.collect_sequences(self.args.episodes_buffer, random=True)
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# Group by steps and sample random batch
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#random_indices = self.data_buffer.sample_random_idx(self.args.batch_size * ((step//self.args.collection_interval)+1)) # random indices for batch
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#random_indices = self.data_buffer.sample_random_idx(self.data_buffer.steps//self.args.episode_length)
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final_idx = self.data_buffer.group_steps(self.data_buffer, "observations").shape[1]
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random_indices = self.data_buffer.sample_random_idx(final_idx, last=True)
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# collect sequences
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non_zero_indices = np.nonzero(self.data_buffer.episode_count)[0]
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current_obs = self.data_buffer.observations[non_zero_indices]
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next_obs = self.data_buffer.next_observations[non_zero_indices]
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actions_raw = self.data_buffer.actions[non_zero_indices]
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rewards = self.data_buffer.rewards[non_zero_indices]
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self.terms = np.where(self.data_buffer.terminals[non_zero_indices]!=0)[0]
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last_observations = self.data_buffer.group_and_sample_random_batch(self.data_buffer,"observations", "cpu", random_indices=random_indices)
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current_observations = self.data_buffer.group_and_sample_random_batch(self.data_buffer,"next_observations", device="cpu", random_indices=random_indices)
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next_observations = self.data_buffer.group_and_sample_random_batch(self.data_buffer,"next_observations", device="cpu", offset=1, random_indices=random_indices)
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actions = self.data_buffer.group_and_sample_random_batch(self.data_buffer,"actions", device=device, is_obs=False, random_indices=random_indices)
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next_actions = self.data_buffer.group_and_sample_random_batch(self.data_buffer,"actions", device=device, is_obs=False, offset=1, random_indices=random_indices)
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rewards = self.data_buffer.group_and_sample_random_batch(self.data_buffer,"rewards", device=device, is_obs=False, offset=1, random_indices=random_indices)
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# Preprocessing
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last_observations = preprocess_obs(last_observations).to(device)
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current_observations = preprocess_obs(current_observations).to(device)
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next_observations = preprocess_obs(next_observations).to(device)
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# Initialize transition model states
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self.transition_model.init_states(self.args.batch_size, device) # (N,128)
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self.history = self.transition_model.prev_history # (N,128)
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# Group by episodes
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current_obs = self.grouped_arrays(current_obs)
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next_obs = self.grouped_arrays(next_obs)
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actions_raw = self.grouped_arrays(actions_raw)
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rewards_ = self.grouped_arrays(rewards)
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# Train encoder
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if step == 0:
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step += 1
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for _ in range(1):#(self.args.collection_interval // self.args.episode_length+1):
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update_steps = 1 if step > 1 else 1
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#for _ in range(self.args.collection_interval // self.args.episode_length+1):
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for _ in range(update_steps):
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counter += 1
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past_encoder_loss = 0
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# Select random chunks of episodes
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if current_obs.shape[0] < self.args.batch_size:
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random_episode_number = np.random.randint(0, current_obs.shape[0], self.args.batch_size)
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else:
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random_episode_number = random.sample(range(current_obs.shape[0]), self.args.batch_size)
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if current_obs[0].shape[0]-self.args.episode_length < self.args.batch_size:
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init_index = np.random.randint(0, current_obs[0].shape[0]-self.args.episode_length-2, self.args.batch_size)
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else:
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init_index = np.asarray(random.sample(range(current_obs[0].shape[0]-self.args.episode_length), self.args.batch_size))
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random.shuffle(random_episode_number)
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random.shuffle(init_index)
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last_observations = self.select_first_k(current_obs, init_index, random_episode_number)[:-1]
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current_observations = self.select_first_k(current_obs, init_index, random_episode_number)[1:]
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next_observations = self.select_first_k(next_obs, init_index, random_episode_number)[:-1]
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actions = self.select_first_k(actions_raw, init_index, random_episode_number)[:-1].to(device)
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next_actions = self.select_first_k(actions_raw, init_index, random_episode_number)[1:].to(device)
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rewards = self.select_first_k(rewards_, init_index, random_episode_number)[1:].to(device)
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# Preprocessing
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last_observations = preprocess_obs(last_observations).to(device)
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current_observations = preprocess_obs(current_observations).to(device)
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next_observations = preprocess_obs(next_observations).to(device)
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# Initialize transition model states
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self.transition_model.init_states(self.args.batch_size, device) # (N,128)
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self.history = self.transition_model.prev_history # (N,128)
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past_world_model_loss = 0
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past_action_loss = 0
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past_value_loss = 0
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for i in range(self.args.episode_length-1):
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if i > 0:
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# Encode observations and next_observations
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@ -323,11 +357,11 @@ class DPI:
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self.next_action = next_actions[i] # (N,6)
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history = self.transition_model.prev_history
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# Encode negative observations
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# Encode negative observations fro upper bound loss
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idx = torch.randperm(current_observations[i].shape[0]) # random permutation on batch
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random_time_index = torch.randint(0, self.args.episode_length-2, (1,)).item() # random time index
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random_time_index = torch.randint(0, current_observations.shape[0]-2, (1,)).item() # random time index
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negative_current_observations = current_observations[random_time_index][idx]
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self.negative_current_states_dict = self.obs_encoder(negative_current_observations)
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self.negative_current_states_dict = self.get_features(negative_current_observations)
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# Predict current state from past state with transition model
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last_states_sample = self.last_states_dict["sample"]
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@ -335,25 +369,24 @@ class DPI:
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self.history = predicted_current_state_dict["history"]
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# Calculate upper bound loss
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likeli_loss, ub_loss = self._upper_bound_minimization(self.last_states_dict,
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self.current_states_dict,
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self.negative_current_states_dict,
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predicted_current_state_dict
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)
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likeli_loss, ub_loss = self._upper_bound_minimization(self.last_states_dict["sample"].detach(),
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self.current_states_dict["sample"].detach(),
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self.negative_current_states_dict["sample"].detach(),
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predicted_current_state_dict["sample"].detach(),
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)
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# Calculate encoder loss
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encoder_loss = past_encoder_loss + self._past_encoder_loss(self.current_states_dict, predicted_current_state_dict)
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past_encoder_loss = encoder_loss.item()
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encoder_loss = self._past_encoder_loss(self.current_states_dict, predicted_current_state_dict)
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# decoder loss
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horizon = np.minimum(self.args.imagine_horizon, self.args.episode_length-1-i)
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nxt_obs = next_observations[i:i+horizon].view(-1,9,84,84)
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nxt_obs = next_observations[i:i+horizon].reshape(-1,9,84,84)
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next_states_encodings = self.get_features(nxt_obs)["sample"].view(horizon,self.args.batch_size, -1)
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obs_dist = self.obs_decoder(next_states_encodings)
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decoder_loss = -torch.mean(obs_dist.log_prob(next_observations[i:i+horizon][:,:,:3,:,:]))
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decoder_loss = -torch.mean(obs_dist.log_prob(next_observations[i:i+horizon]))
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# contrastive projection
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vec_anchor = predicted_current_state_dict["sample"]
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vec_anchor = predicted_current_state_dict["sample"].detach()
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vec_positive = self.next_states_dict["sample"].detach()
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z_anchor = self.prjoection_head(vec_anchor, self.action)
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z_positive = self.prjoection_head_momentum(vec_positive, next_actions[i]).detach()
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@ -363,24 +396,25 @@ class DPI:
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labels = torch.arange(logits.shape[0]).long().to(device)
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lb_loss = F.cross_entropy(logits, labels)
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# reward loss
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# reward loss
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reward_dist = self.reward_model(self.current_states_dict["sample"])
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reward_loss = -torch.mean(reward_dist.log_prob(rewards[:-1]))
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reward_loss = -torch.mean(reward_dist.log_prob(rewards[i]))
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# world model loss
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world_model_loss = encoder_loss + ub_loss + lb_loss + reward_loss + decoder_loss
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world_model_loss = (10*encoder_loss + 10*ub_loss + 1e-1*lb_loss + reward_loss + 1e-3*decoder_loss + past_world_model_loss) * 1e-3
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past_world_model_loss = world_model_loss.item()
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# actor loss
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with FreezeParameters(self.world_model_modules):
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imagine_horizon = self.args.imagine_horizon #np.minimum(self.args.imagine_horizon, self.args.episode_length-1-i)
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action = self.actor_model(self.current_states_dict["sample"])
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imagined_rollout = self.transition_model.imagine_rollout(self.current_states_dict["sample"].detach(),
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action, self.history.detach(),
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imagine_horizon)
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imagined_rollout = self.transition_model.imagine_rollout(self.current_states_dict["sample"],
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action, self.history,
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imagine_horizon)
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with FreezeParameters(self.world_model_modules + self.value_modules):
|
||||
imag_rewards = self.reward_model(imagined_rollout["sample"]).mean
|
||||
imag_values = self.value_model(imagined_rollout["sample"]).mean
|
||||
imag_values = self.target_value_model(imagined_rollout["sample"]).mean
|
||||
|
||||
discounts = self.args.discount * torch.ones_like(imag_rewards).detach()
|
||||
|
||||
@ -392,44 +426,26 @@ class DPI:
|
||||
|
||||
discounts = torch.cat([torch.ones_like(discounts[:1]), discounts[1:-1]], 0)
|
||||
self.discounts = torch.cumprod(discounts, 0).detach()
|
||||
actor_loss = -torch.mean(self.discounts * self.returns)
|
||||
actor_loss = -torch.mean(self.discounts * self.returns) + past_action_loss
|
||||
past_action_loss = actor_loss.item()
|
||||
|
||||
# value loss
|
||||
with torch.no_grad():
|
||||
value_feat = imagined_rollout["sample"][:-1].detach()
|
||||
value_targ = self.returns.detach()
|
||||
|
||||
value_dist = self.value_model(value_feat)
|
||||
value_loss = -torch.mean(self.discounts * value_dist.log_prob(value_targ).unsqueeze(-1))
|
||||
|
||||
# update models
|
||||
self.world_model_opt.zero_grad()
|
||||
self.actor_opt.zero_grad()
|
||||
self.value_opt.zero_grad()
|
||||
|
||||
world_model_loss.backward()
|
||||
actor_loss.backward()
|
||||
value_loss.backward()
|
||||
|
||||
nn.utils.clip_grad_norm_(self.world_model_parameters, self.args.grad_clip_norm)
|
||||
nn.utils.clip_grad_norm_(self.actor_model.parameters(), self.args.grad_clip_norm)
|
||||
nn.utils.clip_grad_norm_(self.value_model.parameters(), self.args.grad_clip_norm)
|
||||
value_dist = self.value_model(value_feat)
|
||||
|
||||
self.world_model_opt.step()
|
||||
self.actor_opt.step()
|
||||
self.value_opt.step()
|
||||
|
||||
# update momentum encoder and projection head
|
||||
soft_update_params(self.obs_encoder, self.obs_encoder_momentum, self.args.encoder_tau)
|
||||
soft_update_params(self.prjoection_head, self.prjoection_head_momentum, self.args.encoder_tau)
|
||||
value_loss = -torch.mean(self.discounts * value_dist.log_prob(value_targ).unsqueeze(-1)) + past_value_loss
|
||||
past_value_loss = value_loss.item()
|
||||
|
||||
# update target value
|
||||
#if step % self.args.value_target_update_freq == 0:
|
||||
# self.target_value_model = copy.deepcopy(self.value_model)
|
||||
if step % self.args.value_target_update_freq == 0:
|
||||
self.target_value_model = copy.deepcopy(self.value_model)
|
||||
|
||||
# counter for reward
|
||||
count = np.arange((counter-1) * (self.args.batch_size), (counter) * (self.args.batch_size))
|
||||
|
||||
#count = np.arange((counter-1) * (self.args.batch_size), (counter) * (self.args.batch_size))
|
||||
count = (counter-1) * (self.args.batch_size)
|
||||
if step % self.args.logging_freq:
|
||||
writer.add_scalar('World Loss/World Loss', world_model_loss.detach().item(), self.data_buffer.steps)
|
||||
writer.add_scalar('Main Models Loss/Encoder Loss', encoder_loss.detach().item(), self.data_buffer.steps)
|
||||
@ -438,27 +454,44 @@ class DPI:
|
||||
writer.add_scalar('Actor Critic Loss/Value Loss', value_loss.detach().item(), self.data_buffer.steps)
|
||||
writer.add_scalar('Actor Critic Loss/Reward Loss', reward_loss.detach().item(), self.data_buffer.steps)
|
||||
writer.add_scalar('Bound Loss/Upper Bound Loss', ub_loss.detach().item(), self.data_buffer.steps)
|
||||
writer.add_scalar('Bound Loss/Lower Bound Loss', lb_loss.detach().item(), self.data_buffer.steps)
|
||||
|
||||
writer.add_scalar('Bound Loss/Lower Bound Loss', lb_loss.detach().item(), self.data_buffer.steps)
|
||||
step += 1
|
||||
|
||||
|
||||
# save model
|
||||
#if step % 500 == 0:#self.args.saving_interval == 0:
|
||||
# print("Saving model")
|
||||
# path = os.path.dirname(os.path.realpath(__file__)) + "/saved_models/models.pth"
|
||||
# self.save_models(path)
|
||||
|
||||
for j in range(len(all_rews)):
|
||||
writer.add_scalar('Rewards/Rewards', all_rews[j], count[j])
|
||||
|
||||
#print(self.data_buffer.steps , ((self.args.episode_length-1) * self.args.batch_size * 5))
|
||||
if self.data_buffer.steps % 5100 == 0 and self.data_buffer.steps!=0: #self.args.evaluation_interval == 0:
|
||||
print("Saving model")
|
||||
path = os.path.dirname(os.path.realpath(__file__)) + "/saved_models/models.pth"
|
||||
self.save_models(path)
|
||||
self.evaluate()
|
||||
print(world_model_loss, actor_loss, value_loss)
|
||||
|
||||
# update actor model
|
||||
self.actor_opt.zero_grad()
|
||||
actor_loss.backward()
|
||||
nn.utils.clip_grad_norm_(self.actor_model.parameters(), self.args.grad_clip_norm)
|
||||
self.actor_opt.step()
|
||||
|
||||
# update world model
|
||||
self.world_model_opt.zero_grad()
|
||||
world_model_loss.backward()
|
||||
nn.utils.clip_grad_norm_(self.world_model_parameters, self.args.grad_clip_norm)
|
||||
self.world_model_opt.step()
|
||||
|
||||
# update value model
|
||||
self.value_opt.zero_grad()
|
||||
value_loss.backward()
|
||||
nn.utils.clip_grad_norm_(self.value_model.parameters(), self.args.grad_clip_norm)
|
||||
self.value_opt.step()
|
||||
|
||||
# update momentum encoder and projection head
|
||||
soft_update_params(self.obs_encoder, self.obs_encoder_momentum, self.args.encoder_tau)
|
||||
soft_update_params(self.prjoection_head, self.prjoection_head_momentum, self.args.encoder_tau)
|
||||
|
||||
rew_len = np.arange(count, count+self.args.episode_collection) if count != 0 else np.arange(0, self.args.batch_size)
|
||||
for j in range(len(all_rews)):
|
||||
writer.add_scalar('Rewards/Rewards', all_rews[j], rew_len[j])
|
||||
|
||||
print(step)
|
||||
if step % 2850 == 0 and self.data_buffer.steps!=0: #self.args.evaluation_interval == 0:
|
||||
print("Saving model")
|
||||
path = os.path.dirname(os.path.realpath(__file__)) + "/saved_models/models.pth"
|
||||
self.save_models(path)
|
||||
self.evaluate()
|
||||
|
||||
|
||||
def evaluate(self, eval_episodes=10):
|
||||
path = path = os.path.dirname(os.path.realpath(__file__)) + "/saved_models/models.pth"
|
||||
@ -476,9 +509,10 @@ class DPI:
|
||||
done = False
|
||||
while not done:
|
||||
with torch.no_grad():
|
||||
obs_torch = torch.unsqueeze(torch.tensor(obs).float(),0).to(device)
|
||||
state = self.obs_encoder(obs_torch)["distribution"].sample()
|
||||
action = self.actor_model(state).cpu().detach().numpy().squeeze()
|
||||
obs = torch.tensor(obs.copy(), dtype=torch.float32).unsqueeze(0)
|
||||
obs_processed = preprocess_obs(obs).to(device)
|
||||
state = self.obs_encoder(obs_processed)["distribution"].sample()
|
||||
action = self.actor_model(state).cpu().detach().numpy().squeeze()
|
||||
|
||||
next_obs, rew, done, _ = self.env.step(action)
|
||||
rewards += rew
|
||||
@ -491,16 +525,51 @@ class DPI:
|
||||
episodic_rewards.append(rewards)
|
||||
print("Episodic rewards: ", episodic_rewards)
|
||||
print("Average episodic reward: ", np.mean(episodic_rewards))
|
||||
|
||||
|
||||
def init_weights(self, m):
|
||||
if isinstance(m, nn.Linear):
|
||||
torch.nn.init.xavier_uniform_(m.weight)
|
||||
m.bias.data.fill_(0.01)
|
||||
|
||||
|
||||
def grouped_arrays(self,array):
|
||||
indices = [0] + self.terms.tolist()
|
||||
|
||||
def subarrays():
|
||||
for start, end in zip(indices[:-1], indices[1:]):
|
||||
yield array[start:end]
|
||||
|
||||
try:
|
||||
subarrays = np.stack(list(subarrays()), axis=0)
|
||||
except ValueError:
|
||||
subarrays = np.asarray(list(subarrays()))
|
||||
|
||||
return subarrays
|
||||
|
||||
def select_first_k(self, array, init_index, episode_number):
|
||||
term_index = init_index + self.args.episode_length
|
||||
array = array[episode_number]
|
||||
|
||||
array_list = []
|
||||
for i in range(array.shape[0]):
|
||||
array_list.append(array[i][init_index[i]:term_index[i]])
|
||||
array = np.asarray(array_list)
|
||||
|
||||
if array.ndim == 5:
|
||||
transposed_array = np.transpose(array, (1, 0, 2, 3, 4))
|
||||
elif array.ndim == 4:
|
||||
transposed_array = np.transpose(array, (1, 0, 2, 3))
|
||||
elif array.ndim == 3:
|
||||
transposed_array = np.transpose(array, (1, 0, 2))
|
||||
elif array.ndim == 2:
|
||||
transposed_array = np.transpose(array, (1, 0))
|
||||
else:
|
||||
transposed_array = np.expand_dims(array, axis=0)
|
||||
return torch.tensor(transposed_array).float()
|
||||
|
||||
def _upper_bound_minimization(self, last_states, current_states, negative_current_states, predicted_current_states):
|
||||
club_sample = CLUBSample(last_states,
|
||||
current_states,
|
||||
negative_current_states,
|
||||
predicted_current_states)
|
||||
likelihood_loss = club_sample.learning_loss()
|
||||
club_loss = club_sample()
|
||||
club_loss = self.club_sample(current_states, predicted_current_states, negative_current_states)
|
||||
likelihood_loss = 0
|
||||
return likelihood_loss, club_loss
|
||||
|
||||
def _past_encoder_loss(self, curr_states_dict, predicted_curr_states_dict):
|
||||
@ -511,7 +580,7 @@ class DPI:
|
||||
predicted_curr_states_dist = predicted_curr_states_dict["distribution"]
|
||||
|
||||
# KL divergence loss
|
||||
loss = torch.mean(torch.distributions.kl.kl_divergence(curr_states_dist, predicted_curr_states_dist))
|
||||
loss = torch.mean(torch.distributions.kl.kl_divergence(curr_states_dist,predicted_curr_states_dist))
|
||||
|
||||
return loss
|
||||
|
||||
@ -573,7 +642,7 @@ if __name__ == '__main__':
|
||||
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
|
||||
|
||||
step = 0
|
||||
total_steps = 200000
|
||||
total_steps = 500000
|
||||
dpi = DPI(args)
|
||||
dpi.train(step,total_steps)
|
||||
dpi.evaluate()
|
||||
Loading…
Reference in New Issue
Block a user