Trying some ideas
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9a2e9f420b
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284
DPI/train.py
284
DPI/train.py
@ -48,11 +48,11 @@ def parse_args():
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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=10000, type=int)
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parser.add_argument('--batch_size', default=30, type=int) #512
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parser.add_argument('--state_size', default=256, type=int)
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parser.add_argument('--hidden_size', default=128, type=int)
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parser.add_argument('--history_size', default=128, 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('--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('--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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@ -60,42 +60,33 @@ def parse_args():
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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=8e-5, type=float)
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parser.add_argument('--value_lr', default=1e-3, 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=8e-5, type=float)
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parser.add_argument('--actor_lr', default=1e-3, 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('--encoder_feature_dim', default=50, type=int)
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parser.add_argument('--world_model_lr', default=6e-4, type=float)
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parser.add_argument('--past_transition_lr', default=1e-3, type=float)
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parser.add_argument('--encoder_lr', default=1e-3, type=float)
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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('--encoder_stride', default=1, type=int)
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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('--decoder_lr', default=1e-3, type=float)
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parser.add_argument('--decoder_update_freq', default=1, type=int)
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parser.add_argument('--decoder_weight_lambda', default=0.0, type=float)
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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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parser.add_argument('--aug', action='store_true')
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# sac
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parser.add_argument('--discount', default=0.99, type=float)
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parser.add_argument('--init_temperature', default=0.01, type=float)
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parser.add_argument('--alpha_lr', default=1e-3, type=float)
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parser.add_argument('--alpha_beta', default=0.9, type=float)
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# misc
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parser.add_argument('--seed', default=1, type=int)
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parser.add_argument('--logging_freq', default=100, type=int)
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parser.add_argument('--saving_interval', default=1000, type=int)
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parser.add_argument('--saving_interval', default=2500, type=int)
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parser.add_argument('--work_dir', default='.', type=str)
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parser.add_argument('--save_tb', default=False, action='store_true')
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parser.add_argument('--save_model', default=False, action='store_true')
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@ -221,19 +212,18 @@ class DPI:
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# model parameters
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self.world_model_parameters = list(self.obs_encoder.parameters()) + list(self.obs_decoder.parameters()) + \
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list(self.value_model.parameters()) + list(self.transition_model.parameters()) + \
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list(self.prjoection_head.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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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.past_transition_opt = torch.optim.Adam(self.past_transition_parameters, self.args.past_transition_lr)
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# Create Modules
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self.world_model_modules = [self.obs_encoder, self.obs_decoder, self.reward_model, self.transition_model, self.prjoection_head]
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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.value_modules = [self.value_model]
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self.actor_modules = [self.actor_model]
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@ -252,8 +242,8 @@ class DPI:
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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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#if args.save_video:
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# self.env.video.init(enabled=True)
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epi_reward = 0
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for i in range(self.args.episode_length):
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@ -263,24 +253,24 @@ class DPI:
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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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action = self.actor_model(state).cpu().detach().numpy().squeeze()
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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 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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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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epi_reward += rew
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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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#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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@ -291,14 +281,16 @@ class DPI:
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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(self.args.batch_size, random=True)
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all_rews = self.collect_sequences(self.args.batch_size, random=False, actor_model=actor, encoder_model=encoder)
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all_rews = self.collect_sequences(10, 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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# 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 = np.arange(self.args.batch_size * ((step//self.args.collection_interval)),self.args.batch_size * ((step//self.args.collection_interval)+1))
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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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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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@ -318,8 +310,9 @@ class DPI:
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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(self.args.collection_interval // self.args.episode_length+1):
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for _ in range(1):#(self.args.collection_interval // self.args.episode_length+1):
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counter += 1
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past_encoder_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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@ -349,8 +342,15 @@ class DPI:
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)
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# Calculate encoder loss
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encoder_loss = self._past_encoder_loss(self.current_states_dict,
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predicted_current_state_dict)
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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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# 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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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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# contrastive projection
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vec_anchor = predicted_current_state_dict["sample"]
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@ -362,136 +362,137 @@ class DPI:
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logits = self.contrastive_head(z_anchor, z_positive)
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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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# behaviour learning
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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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imagined_rollout = self.transition_model.imagine_rollout(self.current_states_dict["sample"].detach(),
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self.next_action, self.history.detach(),
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imagine_horizon)
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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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obs_dist = self.obs_decoder(imagined_rollout["sample"][:horizon])
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decoder_loss = -torch.mean(obs_dist.log_prob(next_observations[i:i+horizon][:,:,:3,:,:]))
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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[:-1]))
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# update models
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world_model_loss = encoder_loss + 100 * ub_loss + lb_loss + reward_loss + decoder_loss * 1e-2
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self.world_model_opt.zero_grad()
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world_model_loss.backward()
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nn.utils.clip_grad_norm_(self.world_model_parameters, self.args.grad_clip_norm)
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self.world_model_opt.step()
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# update momentum encoder
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soft_update_params(self.obs_encoder, self.obs_encoder_momentum, self.args.encoder_tau)
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# update momentum projection head
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soft_update_params(self.prjoection_head, self.prjoection_head_momentum, self.args.encoder_tau)
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# actor loss
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with FreezeParameters(self.world_model_modules + self.value_modules):
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imag_rew_dist = self.reward_model(imagined_rollout["sample"])
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target_imag_val_dist = self.target_value_model(imagined_rollout["sample"])
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imag_rews = imag_rew_dist.mean
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target_imag_vals = target_imag_val_dist.mean
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discounts = self.args.discount * torch.ones_like(imag_rews).detach()
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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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self.target_returns = self._compute_lambda_return(imag_rews[:-1],
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target_imag_vals[:-1],
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discounts[:-1] ,
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self.args.td_lambda,
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target_imag_vals[-1])
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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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with FreezeParameters(self.world_model_modules + self.value_modules):
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imag_rewards = self.reward_model(imagined_rollout["sample"]).mean
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imag_values = self.value_model(imagined_rollout["sample"]).mean
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discounts = self.args.discount * torch.ones_like(imag_rewards).detach()
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self.returns = self._compute_lambda_return(imag_rewards[:-1],
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imag_values[:-1],
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discounts[:-1] ,
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self.args.td_lambda,
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imag_values[-1])
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discounts = torch.cat([torch.ones_like(discounts[:1]), discounts[1:-1]], 0)
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self.discounts = torch.cumprod(discounts, 0).detach()
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actor_loss = -torch.mean(self.discounts * self.target_returns)
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actor_loss = -torch.mean(self.discounts * self.returns)
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# update actor
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self.actor_opt.zero_grad()
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actor_loss.backward()
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nn.utils.clip_grad_norm_(self.actor_model.parameters(), self.args.grad_clip_norm)
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self.actor_opt.step()
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# value loss
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with torch.no_grad():
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value_feat = imagined_rollout["sample"][:-1].detach()
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value_targ = self.target_returns.detach()
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value_targ = self.returns.detach()
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value_dist = self.value_model(value_feat)
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value_loss = -torch.mean(self.discounts * value_dist.log_prob(value_targ).unsqueeze(-1))
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# update value
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# update models
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self.world_model_opt.zero_grad()
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self.actor_opt.zero_grad()
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self.value_opt.zero_grad()
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world_model_loss.backward()
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actor_loss.backward()
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value_loss.backward()
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nn.utils.clip_grad_norm_(self.world_model_parameters, self.args.grad_clip_norm)
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nn.utils.clip_grad_norm_(self.actor_model.parameters(), self.args.grad_clip_norm)
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nn.utils.clip_grad_norm_(self.value_model.parameters(), self.args.grad_clip_norm)
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self.world_model_opt.step()
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self.actor_opt.step()
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self.value_opt.step()
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# update momentum encoder and projection head
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soft_update_params(self.obs_encoder, self.obs_encoder_momentum, self.args.encoder_tau)
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soft_update_params(self.prjoection_head, self.prjoection_head_momentum, self.args.encoder_tau)
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# update target value
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if step % self.args.value_target_update_freq == 0:
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self.target_value_model = copy.deepcopy(self.value_model)
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#if step % self.args.value_target_update_freq == 0:
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# self.target_value_model = copy.deepcopy(self.value_model)
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# counter for reward
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count = np.arange((counter-1) * (self.args.batch_size), (counter) * (self.args.batch_size))
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if step % self.args.logging_freq:
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writer.add_scalar('World Loss/World Loss', world_model_loss.detach().item(), step)
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writer.add_scalar('Main Models Loss/Encoder Loss', encoder_loss.detach().item(), step)
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writer.add_scalar('Main Models Loss/Decoder Loss', decoder_loss, step)
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writer.add_scalar('Actor Critic Loss/Actor Loss', actor_loss.detach().item(), step)
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writer.add_scalar('Actor Critic Loss/Value Loss', value_loss.detach().item(), step)
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writer.add_scalar('Actor Critic Loss/Reward Loss', reward_loss.detach().item(), step)
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writer.add_scalar('Bound Loss/Upper Bound Loss', ub_loss.detach().item(), step)
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writer.add_scalar('Bound Loss/Lower Bound Loss', lb_loss.detach().item(), step)
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writer.add_scalar('World Loss/World Loss', world_model_loss.detach().item(), self.data_buffer.steps)
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writer.add_scalar('Main Models Loss/Encoder Loss', encoder_loss.detach().item(), self.data_buffer.steps)
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writer.add_scalar('Main Models Loss/Decoder Loss', decoder_loss, self.data_buffer.steps)
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writer.add_scalar('Actor Critic Loss/Actor Loss', actor_loss.detach().item(), self.data_buffer.steps)
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writer.add_scalar('Actor Critic Loss/Value Loss', value_loss.detach().item(), self.data_buffer.steps)
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writer.add_scalar('Actor Critic Loss/Reward Loss', reward_loss.detach().item(), self.data_buffer.steps)
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writer.add_scalar('Bound Loss/Upper Bound Loss', ub_loss.detach().item(), self.data_buffer.steps)
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writer.add_scalar('Bound Loss/Lower Bound Loss', lb_loss.detach().item(), self.data_buffer.steps)
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step += 1
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if step>total_steps:
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print("Training finished")
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break
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# save model
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if step % self.args.saving_interval == 0:
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path = os.path.dirname(os.path.realpath(__file__)) + "/saved_models/models.pth"
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self.save_models(path)
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#torch.cuda.empty_cache() # memory leak issues
|
||||
|
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# save model
|
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#if step % 500 == 0:#self.args.saving_interval == 0:
|
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# print("Saving model")
|
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# path = os.path.dirname(os.path.realpath(__file__)) + "/saved_models/models.pth"
|
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# self.save_models(path)
|
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|
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for j in range(len(all_rews)):
|
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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))
|
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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()
|
||||
|
||||
|
||||
def evaluate(self, env, eval_episodes, render=False):
|
||||
def evaluate(self, eval_episodes=10):
|
||||
path = path = os.path.dirname(os.path.realpath(__file__)) + "/saved_models/models.pth"
|
||||
self.restore_checkpoint(path)
|
||||
|
||||
episode_rew = np.zeros((eval_episodes))
|
||||
obs = self.env.reset()
|
||||
done = False
|
||||
|
||||
video_images = [[] for _ in range(eval_episodes)]
|
||||
|
||||
for i in range(eval_episodes):
|
||||
obs = env.reset()
|
||||
#video = VideoRecorder(self.video_dir, resource_files=self.args.resource_files)
|
||||
if self.args.save_video:
|
||||
self.env.video.init(enabled=True)
|
||||
episodic_rewards = []
|
||||
for episode in range(eval_episodes):
|
||||
rewards = 0
|
||||
done = False
|
||||
prev_state = self.rssm.init_state(1, self.device)
|
||||
prev_action = torch.zeros(1, self.action_size).to(self.device)
|
||||
|
||||
while not done:
|
||||
with torch.no_grad():
|
||||
posterior, action = self.act_with_world_model(obs, prev_state, prev_action)
|
||||
action = action[0].cpu().numpy()
|
||||
next_obs, rew, done, _ = env.step(action)
|
||||
prev_state = posterior
|
||||
prev_action = torch.tensor(action, dtype=torch.float32).to(self.device).unsqueeze(0)
|
||||
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()
|
||||
|
||||
next_obs, rew, done, _ = self.env.step(action)
|
||||
rewards += rew
|
||||
|
||||
episode_rew[i] += rew
|
||||
|
||||
if render:
|
||||
video_images[i].append(obs['image'].transpose(1,2,0).copy())
|
||||
if self.args.save_video:
|
||||
self.env.video.record(self.env)
|
||||
self.env.video.save('/home/vedant/Curiosity/Curiosity/DPI/log/video/learned_model.mp4')
|
||||
obs = next_obs
|
||||
return episode_rew, np.array(video_images[:self.args.max_videos_to_save])
|
||||
obs = self.env.reset()
|
||||
episodic_rewards.append(rewards)
|
||||
print("Episodic rewards: ", episodic_rewards)
|
||||
print("Average episodic reward: ", np.mean(episodic_rewards))
|
||||
|
||||
|
||||
|
||||
def _upper_bound_minimization(self, last_states, current_states, negative_current_states, predicted_current_states):
|
||||
club_sample = CLUBSample(last_states,
|
||||
@ -510,15 +511,16 @@ class DPI:
|
||||
predicted_curr_states_dist = predicted_curr_states_dict["distribution"]
|
||||
|
||||
# KL divergence loss
|
||||
loss = torch.distributions.kl.kl_divergence(curr_states_dist, predicted_curr_states_dist).mean()
|
||||
loss = torch.mean(torch.distributions.kl.kl_divergence(curr_states_dist, predicted_curr_states_dist))
|
||||
|
||||
return loss
|
||||
|
||||
def get_features(self, x, momentum=False):
|
||||
if self.args.aug:
|
||||
x = T.RandomCrop((80, 80))(x) # (None,80,80,4)
|
||||
x = T.functional.pad(x, (4, 4, 4, 4), "symmetric") # (None,88,88,4)
|
||||
x = T.RandomCrop((84, 84))(x) # (None,84,84,4)
|
||||
crop_transform = T.RandomCrop(size=80)
|
||||
cropped_x = torch.stack([crop_transform(x[i]) for i in range(x.size(0))])
|
||||
padding = (2, 2, 2, 2)
|
||||
x = F.pad(cropped_x, padding)
|
||||
|
||||
with torch.no_grad():
|
||||
if momentum:
|
||||
@ -550,6 +552,17 @@ class DPI:
|
||||
'actor_optimizer': self.actor_opt.state_dict(),
|
||||
'value_optimizer': self.value_opt.state_dict(),
|
||||
'world_model_optimizer': self.world_model_opt.state_dict(),}, save_path)
|
||||
|
||||
def restore_checkpoint(self, ckpt_path):
|
||||
checkpoint = torch.load(ckpt_path)
|
||||
self.transition_model.load_state_dict(checkpoint['rssm'])
|
||||
self.actor_model.load_state_dict(checkpoint['actor'])
|
||||
self.reward_model.load_state_dict(checkpoint['reward_model'])
|
||||
self.obs_encoder.load_state_dict(checkpoint['obs_encoder'])
|
||||
self.obs_decoder.load_state_dict(checkpoint['obs_decoder'])
|
||||
self.world_model_opt.load_state_dict(checkpoint['world_model_optimizer'])
|
||||
self.actor_opt.load_state_dict(checkpoint['actor_optimizer'])
|
||||
self.value_opt.load_state_dict(checkpoint['value_optimizer'])
|
||||
|
||||
if __name__ == '__main__':
|
||||
args = parse_args()
|
||||
@ -560,6 +573,7 @@ if __name__ == '__main__':
|
||||
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
|
||||
|
||||
step = 0
|
||||
total_steps = 10000
|
||||
total_steps = 200000
|
||||
dpi = DPI(args)
|
||||
dpi.train(step,total_steps)
|
||||
dpi.train(step,total_steps)
|
||||
dpi.evaluate()
|
||||
Loading…
Reference in New Issue
Block a user