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97
DPI/models.py
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@ -194,27 +194,6 @@ class TransitionModel(nn.Module):
prior = {"mean": state_prior_mean, "std": state_prior_std, "sample": sample_state_prior, "history": history, "distribution": state_prior_dist} prior = {"mean": state_prior_mean, "std": state_prior_std, "sample": sample_state_prior, "history": history, "distribution": state_prior_dist}
return prior return prior
def stack_states(self, states, dim=0):
s = dict(
mean = torch.stack([state['mean'] for state in states], dim=dim),
std = torch.stack([state['std'] for state in states], dim=dim),
sample = torch.stack([state['sample'] for state in states], dim=dim),
history = torch.stack([state['history'] for state in states], dim=dim),)
dist = dict(distribution = [state['distribution'] for state in states])
s.update(dist)
return s
def imagine_rollout(self, state, action, history, horizon):
imagined_priors = []
for i in range(horizon):
prior = self.imagine_step(state, action, history)
state = prior["sample"]
history = prior["history"]
imagined_priors.append(prior)
imagined_priors = self.stack_states(imagined_priors, dim=0)
return imagined_priors
def reparemeterize(self, mean, std): def reparemeterize(self, mean, std):
eps = torch.randn_like(std) eps = torch.randn_like(std)
return mean + eps * std return mean + eps * std
@ -248,6 +227,40 @@ class TanhBijector(torch.distributions.Transform):
return 2.0 * (torch.log(torch.tensor([2.0])) - x - F.softplus(-2.0 * x)) return 2.0 * (torch.log(torch.tensor([2.0])) - x - F.softplus(-2.0 * x))
class CLUBSample(nn.Module): # Sampled version of the CLUB estimator
def __init__(self, x_dim, y_dim, hidden_size):
super(CLUBSample, self).__init__()
self.p_mu = nn.Sequential(
nn.Linear(x_dim, hidden_size//2),
nn.ReLU(),
nn.Linear(hidden_size//2, hidden_size//2),
nn.ReLU(),
nn.Linear(hidden_size//2, y_dim)
)
self.p_logvar = nn.Sequential(
nn.Linear(x_dim, hidden_size//2),
nn.ReLU(),
nn.Linear(hidden_size//2, hidden_size//2),
nn.ReLU(),
nn.Linear(hidden_size//2, y_dim),
nn.Tanh()
)
def get_mu_logvar(self, x_samples):
mu = self.p_mu(x_samples)
logvar = self.p_logvar(x_samples)
return mu, logvar
def loglikeli(self, x_samples, y_samples):
mu, logvar = self.get_mu_logvar(x_samples)
return (-(mu - y_samples)**2 /logvar.exp()-logvar).sum(dim=1).mean(dim=0)
def forward(self, x_samples, y_samples):
mu, logvar = self.get_mu_logvar(x_samples)
return - self.loglikeli(x_samples, y_samples)
class ProjectionHead(nn.Module): class ProjectionHead(nn.Module):
def __init__(self, state_size, action_size, hidden_size): def __init__(self, state_size, action_size, hidden_size):
super(ProjectionHead, self).__init__() super(ProjectionHead, self).__init__()
@ -281,44 +294,4 @@ class ContrastiveHead(nn.Module):
logits = torch.matmul(z_a, Wz) # (B,B) logits = torch.matmul(z_a, Wz) # (B,B)
logits = logits - torch.max(logits, 1)[0][:, None] logits = logits - torch.max(logits, 1)[0][:, None]
logits = logits * self.temperature logits = logits * self.temperature
return logits return logits
class CLUBSample(nn.Module): # Sampled version of the CLUB estimator
def __init__(self, last_states, current_states, negative_current_states, predicted_current_states):
super(CLUBSample, self).__init__()
self.last_states = last_states
self.current_states = current_states
self.negative_current_states = negative_current_states
self.predicted_current_states = predicted_current_states
def get_mu_var_samples(self, state_dict):
dist = state_dict["distribution"]
sample = dist.sample() # Use state_dict["sample"] if you want to use the same sample for all the losses
mu = dist.mean
var = dist.variance
return mu, var, sample
def loglikeli(self):
_, _, pred_sample = self.get_mu_var_samples(self.predicted_current_states)
mu_curr, var_curr, _ = self.get_mu_var_samples(self.current_states)
logvar_curr = torch.log(var_curr)
return (-(mu_curr - pred_sample)**2 /var_curr-logvar_curr).sum(dim=1).mean(dim=0)
def forward(self):
_, _, pred_sample = self.get_mu_var_samples(self.predicted_current_states)
mu_curr, var_curr, _ = self.get_mu_var_samples(self.current_states)
mu_neg, var_neg, _ = self.get_mu_var_samples(self.negative_current_states)
pos = (-(mu_curr - pred_sample)**2 /var_curr).sum(dim=1).mean(dim=0)
neg = (-(mu_neg - pred_sample)**2 /var_neg).sum(dim=1).mean(dim=0)
upper_bound = pos - neg
return upper_bound/2
def learning_loss(self):
return - self.loglikeli()
if "__name__ == __main__":
pass

148
DPI/train.py
View File

@ -16,8 +16,6 @@ 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
@ -33,7 +31,7 @@ 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=3, type=int) parser.add_argument('--frame_stack', default=4, 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'])
@ -41,18 +39,18 @@ 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=51, type=int) parser.add_argument('--episode_length', default=50, 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=10000, type=int) parser.add_argument('--init_steps', default=1000, type=int)
parser.add_argument('--num_train_steps', default=10000, type=int) parser.add_argument('--num_train_steps', default=1000, type=int)
parser.add_argument('--batch_size', default=20, type=int) #512 parser.add_argument('--batch_size', default=200, 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('--imagine_horizon', default=15, type=str) parser.add_argument('--imagination_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)
@ -115,7 +113,6 @@ 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
@ -193,123 +190,87 @@ 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, rew, done, _ = self.env.step(action) next_obs, _, 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) self.env.video.record(self.env_clean)
#self.env_clean.video.record(self.env_clean) self.env_clean.video.record(self.env_clean)
if done or i == self.args.episode_length-1: if done:
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 from past to present # Group observations and next_observations by steps
last_observations = torch.Tensor(self.data_buffer.group_steps(self.data_buffer,"observations")).float()[:self.args.episode_length-1] observations = torch.Tensor(self.data_buffer.group_steps(self.data_buffer,"observations")).float()
current_observations = torch.Tensor(self.data_buffer.group_steps(self.data_buffer,"next_observations")).float()[:self.args.episode_length-1] next_observations = torch.Tensor(self.data_buffer.group_steps(self.data_buffer,"next_observations")).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()
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
total_ub_loss = 0 previous_information_loss = 0
total_encoder_loss = 0 previous_encoder_loss = 0
for i in range(self.args.episode_length-1): for i in range(self.args.episode_length):
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
# Encode negative observations
idx = torch.randperm(current_observations[i].shape[0]) # random permutation on batch
random_time_index = torch.randint(0, self.args.episode_length-2, (1,)).item() # random time index
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 # Sample states and next_states
last_states_sample = self.last_states_dict["sample"] self.states = self.states_dist["sample"] # (N,128)
predicted_current_state_dict = self.transition_model.imagine_step(last_states_sample, self.action, self.history) self.next_states = self.next_states_dist["sample"] # (N,128)
self.history = predicted_current_state_dict["history"] self.actions = actions[i] # (N,6)
# Calculate upper bound loss
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
encoder_loss = self._past_encoder_loss(self.current_states_dict,
predicted_current_state_dict)
total_ub_loss += ub_loss # Calculate upper bound loss
total_encoder_loss += encoder_loss past_latent_loss = previous_information_loss + self._upper_bound_minimization(self.states, self.next_states)
imagine_horizon = np.minimum(self.args.imagine_horizon, self.args.episode_length-1-i) # Calculate encoder loss
imagined_rollout = self.transition_model.imagine_rollout(self.current_states_dict["sample"], self.action, self.history, imagine_horizon) past_encoder_loss = previous_encoder_loss + self._past_encoder_loss(self.states, self.next_states,
print(imagine_horizon) self.states_dist, self.next_states_dist,
#exit() self.actions, self.history, i)
#print(total_ub_loss, total_encoder_loss) print(past_encoder_loss, past_latent_loss)
previous_information_loss = past_latent_loss
previous_encoder_loss = past_encoder_loss
def _upper_bound_minimization(self, states, next_states):
def _upper_bound_minimization(self, last_states, current_states, negative_current_states, predicted_current_states): club_sample = CLUBSample(self.args.state_size,
club_sample = CLUBSample(last_states, self.args.state_size,
current_states, self.args.hidden_size)
negative_current_states, club_loss = club_sample(states, next_states)
predicted_current_states)
club_loss = club_sample()
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:
@ -326,21 +287,6 @@ 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()

8
DPI/utils.py
View File

@ -161,11 +161,11 @@ class ReplayBuffer:
non_zero_indices = np.nonzero(buffer.episode_count)[0] non_zero_indices = np.nonzero(buffer.episode_count)[0]
variable = variable[non_zero_indices] variable = variable[non_zero_indices]
if obs: if obs:
variable = variable.reshape(self.args.batch_size, self.args.episode_length, variable = variable.reshape(self.args.episode_length, self.args.batch_size,
self.args.frame_stack*self.args.channels, self.args.frame_stack*self.args.channels,
self.args.image_size,self.args.image_size).transpose(1, 0, 2, 3, 4) self.args.image_size,self.args.image_size)
else: else:
variable = variable.reshape(self.args.batch_size, self.args.episode_length, -1).transpose(1, 0, 2) variable = variable.reshape(self.args.episode_length, self.args.batch_size,-1)
return variable return variable
def transform_grouped_steps(self, variable): def transform_grouped_steps(self, variable):