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Changing Upper Bound loss

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Vedant Dave 2023-04-09 18:22:41 +02:00
parent 5caea7695a
commit 6b4762d5fc
1 changed files with 101 additions and 47 deletions
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140
DPI/train.py
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@ -16,6 +16,8 @@ from logger import Logger
from video import VideoRecorder from video import VideoRecorder
from dmc2gym.wrappers import set_global_var from dmc2gym.wrappers import set_global_var
import torchvision.transforms as T
#from agent.baseline_agent import BaselineAgent #from agent.baseline_agent import BaselineAgent
#from agent.bisim_agent import BisimAgent #from agent.bisim_agent import BisimAgent
#from agent.deepmdp_agent import DeepMDPAgent #from agent.deepmdp_agent import DeepMDPAgent
@ -31,7 +33,7 @@ def parse_args():
parser.add_argument('--image_size', default=84, type=int) parser.add_argument('--image_size', default=84, type=int)
parser.add_argument('--channels', default=3, type=int) parser.add_argument('--channels', default=3, type=int)
parser.add_argument('--action_repeat', default=1, type=int) parser.add_argument('--action_repeat', default=1, type=int)
parser.add_argument('--frame_stack', default=4, type=int) parser.add_argument('--frame_stack', default=3, type=int)
parser.add_argument('--resource_files', type=str) parser.add_argument('--resource_files', type=str)
parser.add_argument('--eval_resource_files', type=str) parser.add_argument('--eval_resource_files', type=str)
parser.add_argument('--img_source', default=None, type=str, choices=['color', 'noise', 'images', 'video', 'none']) parser.add_argument('--img_source', default=None, type=str, choices=['color', 'noise', 'images', 'video', 'none'])
@ -39,18 +41,18 @@ def parse_args():
parser.add_argument('--high_noise', action='store_true') parser.add_argument('--high_noise', action='store_true')
# replay buffer # replay buffer
parser.add_argument('--replay_buffer_capacity', default=50000, type=int) #50000 parser.add_argument('--replay_buffer_capacity', default=50000, type=int) #50000
parser.add_argument('--episode_length', default=50, type=int) parser.add_argument('--episode_length', default=51, type=int)
# train # train
parser.add_argument('--agent', default='dpi', type=str, choices=['baseline', 'bisim', 'deepmdp', 'db', 'dpi', 'rpc']) parser.add_argument('--agent', default='dpi', type=str, choices=['baseline', 'bisim', 'deepmdp', 'db', 'dpi', 'rpc'])
parser.add_argument('--init_steps', default=1000, type=int) parser.add_argument('--init_steps', default=10000, type=int)
parser.add_argument('--num_train_steps', default=1000, type=int) parser.add_argument('--num_train_steps', default=10000, type=int)
parser.add_argument('--batch_size', default=200, type=int) #512 parser.add_argument('--batch_size', default=20, type=int) #512
parser.add_argument('--state_size', default=256, type=int) parser.add_argument('--state_size', default=256, type=int)
parser.add_argument('--hidden_size', default=128, type=int) parser.add_argument('--hidden_size', default=128, type=int)
parser.add_argument('--history_size', default=128, type=int) parser.add_argument('--history_size', default=128, type=int)
parser.add_argument('--num-units', type=int, default=200, help='num hidden units for reward/value/discount models') parser.add_argument('--num-units', type=int, default=200, help='num hidden units for reward/value/discount models')
parser.add_argument('--load_encoder', default=None, type=str) parser.add_argument('--load_encoder', default=None, type=str)
parser.add_argument('--imagination_horizon', default=15, type=str) parser.add_argument('--imagine_horizon', default=15, type=str)
# eval # eval
parser.add_argument('--eval_freq', default=10, type=int) # TODO: master had 10000 parser.add_argument('--eval_freq', default=10, type=int) # TODO: master had 10000
parser.add_argument('--num_eval_episodes', default=20, type=int) parser.add_argument('--num_eval_episodes', default=20, type=int)
@ -113,6 +115,7 @@ class DPI:
# environment setup # environment setup
self.env = make_env(self.args) self.env = make_env(self.args)
#self.args.seed = np.random.randint(0, 1000)
self.env.seed(self.args.seed) self.env.seed(self.args.seed)
# noiseless environment setup # noiseless environment setup
@ -190,87 +193,123 @@ class DPI:
def collect_sequences(self, episodes): def collect_sequences(self, episodes):
obs = self.env.reset() obs = self.env.reset()
obs_clean = self.env_clean.reset() #obs_clean = self.env_clean.reset()
done = False done = False
#video = VideoRecorder(self.video_dir if args.save_video else None, resource_files=args.resource_files) #video = VideoRecorder(self.video_dir if args.save_video else None, resource_files=args.resource_files)
for episode_count in tqdm.tqdm(range(episodes), desc='Collecting episodes'): for episode_count in tqdm.tqdm(range(episodes), desc='Collecting episodes'):
if args.save_video: if args.save_video:
self.env.video.init(enabled=True) self.env.video.init(enabled=True)
self.env_clean.video.init(enabled=True) #self.env_clean.video.init(enabled=True)
for i in range(self.args.episode_length): for i in range(self.args.episode_length):
action = self.env.action_space.sample() action = self.env.action_space.sample()
next_obs, _, done, _ = self.env.step(action) next_obs, rew, done, _ = self.env.step(action)
next_obs_clean, _, done, _ = self.env_clean.step(action) #next_obs_clean, _, done, _ = self.env_clean.step(action)
self.data_buffer.add(obs, action, next_obs, episode_count+1, done) self.data_buffer.add(obs, action, next_obs, episode_count+1, done)
self.data_buffer_clean.add(obs_clean, action, next_obs_clean, episode_count+1, done) #self.data_buffer_clean.add(obs_clean, action, next_obs_clean, episode_count+1, done)
if args.save_video: if args.save_video:
self.env.video.record(self.env_clean) self.env.video.record(self.env)
self.env_clean.video.record(self.env_clean) #self.env_clean.video.record(self.env_clean)
if done: if done or i == self.args.episode_length-1:
obs = self.env.reset() obs = self.env.reset()
obs_clean = self.env_clean.reset() #obs_clean = self.env_clean.reset()
done=False done=False
else: else:
obs = next_obs obs = next_obs
obs_clean = next_obs_clean #obs_clean = next_obs_clean
if args.save_video: if args.save_video:
self.env.video.save('noisy/%d.mp4' % episode_count) self.env.video.save('noisy/%d.mp4' % episode_count)
self.env_clean.video.save('clean/%d.mp4' % episode_count) #self.env_clean.video.save('clean/%d.mp4' % episode_count)
print("Collected {} random episodes".format(episode_count+1)) print("Collected {} random episodes".format(episode_count+1))
def train(self): def train(self):
# collect experience # collect experience
self.collect_sequences(self.args.batch_size) self.collect_sequences(self.args.batch_size)
# Group observations and next_observations by steps # Group observations and next_observations by steps from past to present
observations = torch.Tensor(self.data_buffer.group_steps(self.data_buffer,"observations")).float() last_observations = torch.Tensor(self.data_buffer.group_steps(self.data_buffer,"observations")).float()[:self.args.episode_length-1]
next_observations = torch.Tensor(self.data_buffer.group_steps(self.data_buffer,"next_observations")).float() current_observations = torch.Tensor(self.data_buffer.group_steps(self.data_buffer,"next_observations")).float()[:self.args.episode_length-1]
actions = torch.Tensor(self.data_buffer.group_steps(self.data_buffer,"actions",obs=False)).float() next_observations = torch.Tensor(self.data_buffer.group_steps(self.data_buffer,"next_observations")).float()[1:]
actions = torch.Tensor(self.data_buffer.group_steps(self.data_buffer,"actions",obs=False)).float()[:self.args.episode_length-1]
next_actions = torch.Tensor(self.data_buffer.group_steps(self.data_buffer,"actions",obs=False)).float()[1:]
# Initialize transition model states # Initialize transition model states
self.transition_model.init_states(self.args.batch_size, device="cpu") # (N,128) self.transition_model.init_states(self.args.batch_size, device="cpu") # (N,128)
self.history = self.transition_model.prev_history # (N,128) self.history = self.transition_model.prev_history # (N,128)
# Train encoder # Train encoder
previous_information_loss = 0 total_ub_loss = 0
previous_encoder_loss = 0 total_encoder_loss = 0
for i in range(self.args.episode_length): for i in range(self.args.episode_length-1):
if i > 0:
# Encode observations and next_observations # Encode observations and next_observations
self.states_dist = self.obs_encoder(observations[i]) self.last_states_dict = self.obs_encoder(last_observations[i])
self.next_states_dist = self.obs_encoder(next_observations[i]) self.current_states_dict = self.obs_encoder(current_observations[i])
self.next_states_dict = self.obs_encoder_momentum(next_observations[i])
self.action = actions[i] # (N,6)
history = self.transition_model.prev_history
# Sample states and next_states # Encode negative observations
self.states = self.states_dist["sample"] # (N,128) idx = torch.randperm(current_observations[i].shape[0]) # random permutation on batch
self.next_states = self.next_states_dist["sample"] # (N,128) random_time_index = torch.randint(0, self.args.episode_length-2, (1,)).item() # random time index
self.actions = actions[i] # (N,6) negative_current_observations = current_observations[random_time_index][idx]
self.negative_current_states_dict = self.obs_encoder(negative_current_observations)
# Predict current state from past state with transition model
last_states_sample = self.last_states_dict["sample"]
predicted_current_state_dict = self.transition_model.imagine_step(last_states_sample, self.action, self.history)
self.history = predicted_current_state_dict["history"]
# Calculate upper bound loss # Calculate upper bound loss
past_latent_loss = previous_information_loss + self._upper_bound_minimization(self.states, self.next_states) ub_loss = self._upper_bound_minimization(self.last_states_dict,
self.current_states_dict,
self.negative_current_states_dict,
predicted_current_state_dict
)
# Calculate encoder loss # Calculate encoder loss
past_encoder_loss = previous_encoder_loss + self._past_encoder_loss(self.states, self.next_states, encoder_loss = self._past_encoder_loss(self.current_states_dict,
self.states_dist, self.next_states_dist, predicted_current_state_dict)
self.actions, self.history, i)
total_ub_loss += ub_loss
total_encoder_loss += encoder_loss
imagine_horizon = np.minimum(self.args.imagine_horizon, self.args.episode_length-1-i)
imagined_rollout = self.transition_model.imagine_rollout(self.current_states_dict["sample"], self.action, self.history, imagine_horizon)
print(imagine_horizon)
#exit()
#print(total_ub_loss, total_encoder_loss)
print(past_encoder_loss, past_latent_loss)
previous_information_loss = past_latent_loss def _upper_bound_minimization(self, last_states, current_states, negative_current_states, predicted_current_states):
previous_encoder_loss = past_encoder_loss club_sample = CLUBSample(last_states,
current_states,
def _upper_bound_minimization(self, states, next_states): negative_current_states,
club_sample = CLUBSample(self.args.state_size, predicted_current_states)
self.args.state_size, club_loss = club_sample()
self.args.hidden_size)
club_loss = club_sample(states, next_states)
return club_loss return club_loss
def _past_encoder_loss(self, curr_states_dict, predicted_curr_states_dict):
# current state distribution
curr_states_dist = curr_states_dict["distribution"]
# predicted current state distribution
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()
return loss
"""
def _past_encoder_loss(self, states, next_states, states_dist, next_states_dist, actions, history, step): def _past_encoder_loss(self, states, next_states, states_dist, next_states_dist, actions, history, step):
# Imagine next state # Imagine next state
if step == 0: if step == 0:
@ -287,6 +326,21 @@ class DPI:
loss = torch.distributions.kl.kl_divergence(imagined_next_states_dist, next_states_dist["distribution"]).mean() loss = torch.distributions.kl.kl_divergence(imagined_next_states_dist, next_states_dist["distribution"]).mean()
return loss return loss
"""
def get_features(self, x, momentum=False):
if self.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)
with torch.no_grad():
x = (x.float() - self.ob_mean) / self.ob_std
if momentum:
x = self.obs_encoder(x).detach()
else:
x = self.obs_encoder_momentum(x)
return x
if __name__ == '__main__': if __name__ == '__main__':
args = parse_args() args = parse_args()