Adding Files

encoder.py

@@ -109,6 +109,10 @@ class PixelEncoder(nn.Module):
         out_dim = OUT_DIM[num_layers]
         self.fc = nn.Linear(num_filters * out_dim * out_dim, self.feature_dim * 2)
         self.ln = nn.LayerNorm(self.feature_dim * 2)
+<<<<<<< HEAD
+        self.combine = nn.Linear(self.feature_dim + 6, self.feature_dim)
+=======
+>>>>>>> origin/tester_1
 
         self.outputs = dict()
 
@@ -153,7 +157,11 @@ class PixelEncoder(nn.Module):
 
         out = self.reparameterize(mu, logstd)
         self.outputs['tanh'] = out
+<<<<<<< HEAD
+        return out, mu, logstd
+=======
         return out
+>>>>>>> origin/tester_1
     
     def copy_conv_weights_from(self, source):
         """Tie convolutional layers"""
@@ -202,3 +210,101 @@ def make_encoder(
     return _AVAILABLE_ENCODERS[encoder_type](
         obs_shape, feature_dim, num_layers, num_filters
     )
+
+def club_loss(x_samples, x_mu, x_logvar, y_samples):        
+        sample_size = x_samples.shape[0]
+        random_index = torch.randperm(sample_size).long()
+        
+        positive = -(x_mu - y_samples)**2 / x_logvar.exp()
+        negative = - (x_mu - y_samples[random_index])**2 / x_logvar.exp()
+        upper_bound = (positive.sum(dim = -1) - negative.sum(dim = -1)).mean()
+        return upper_bound/2.
+
+
+class TransitionModel(nn.Module):
+    def __init__(self, state_size, hidden_size, action_size, history_size):
+        super().__init__()
+
+        self.state_size = state_size
+        self.hidden_size = hidden_size
+        self.action_size = action_size
+        self.history_size = history_size
+        self.act_fn = nn.ELU()
+
+        self.fc_state_action = nn.Linear(state_size + action_size, hidden_size)
+        self.fc_hidden = nn.Linear(hidden_size, hidden_size)
+        self.history_cell = nn.GRUCell(hidden_size, history_size)
+        self.fc_state_mu = nn.Linear(history_size + hidden_size, state_size)
+        self.fc_state_sigma = nn.Linear(history_size + hidden_size, state_size)
+
+        self.min_sigma = 1e-4
+        self.max_sigma = 1e0
+
+    def init_states(self, batch_size, device):
+        self.prev_state = torch.zeros(batch_size, self.state_size).to(device)
+        self.prev_action = torch.zeros(batch_size, self.action_size).to(device)
+        self.prev_history = torch.zeros(batch_size, self.history_size).to(device)
+            
+    def get_dist(self, mean, std):
+        distribution = torch.distributions.Normal(mean, std)
+        return distribution
+
+    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),)
+        if 'distribution' in states:
+            dist = dict(distribution = [state['distribution'] for state in states])
+            s.update(dist)
+        return s
+    
+    def seq_to_batch(self, state, name):
+        return dict(
+            sample = torch.reshape(state[name], (state[name].shape[0]* state[name].shape[1], *state[name].shape[2:])))
+
+    def transition_step(self, state, action, hist, not_done):
+        state = state * not_done
+        hist = hist * not_done
+
+        state_action_enc = self.act_fn(self.fc_state_action(torch.cat([state, action], dim=-1)))
+        state_action_enc = self.act_fn(self.fc_hidden(state_action_enc))
+        state_action_enc = self.act_fn(self.fc_hidden(state_action_enc))
+        state_action_enc = self.act_fn(self.fc_hidden(state_action_enc))
+
+        current_hist = self.history_cell(state_action_enc, hist)
+        next_state_mu = self.act_fn(self.fc_state_mu(torch.cat([state_action_enc, hist], dim=-1)))
+        next_state_sigma = torch.tanh(self.fc_state_sigma(torch.cat([state_action_enc, hist], dim=-1)))
+        next_state = next_state_mu + torch.randn_like(next_state_mu) * next_state_sigma.exp()
+
+        state_enc = {"mean": next_state_mu, "logvar": next_state_sigma, "sample": next_state, "history": current_hist}
+        return state_enc
+
+    def observe_rollout(self, rollout_states, rollout_actions, init_history, nonterms):
+        observed_rollout = []
+        for i in range(rollout_states.shape[0]):
+            rollout_states_ = rollout_states[i]
+            rollout_actions_ = rollout_actions[i]
+            init_history_ = nonterms[i] * init_history
+            state_enc = self.observe_step(rollout_states_, rollout_actions_, init_history_)
+            init_history = state_enc["history"]
+            observed_rollout.append(state_enc)
+        observed_rollout = self.stack_states(observed_rollout, dim=0)
+        return observed_rollout
+    
+    def forward(self, state, action, hist, not_done):
+        return self.transition_step(state, action, hist, not_done)
+
+    def reparameterize(self, mean, std):
+        eps = torch.randn_like(mean)
+        return mean + eps * std
+    
+def club_loss(x_samples, x_mu, x_logvar, y_samples):        
+        sample_size = x_samples.shape[0]
+        random_index = torch.randperm(sample_size).long()
+        
+        positive = -(x_mu - y_samples)**2 / x_logvar.exp()
+        negative = - (x_mu - y_samples[random_index])**2 / x_logvar.exp()
+        upper_bound = (positive.sum(dim = -1) - negative.sum(dim = -1)).mean()
+        return upper_bound/2.0

graphs_plot.py

@@ -0,0 +1,49 @@
+import os
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+from tensorboard.backend.event_processing.event_accumulator import EventAccumulator
+
+
+def tabulate_events(dpath):
+    files = os.listdir(dpath)[0]
+    summary_iterators = [EventAccumulator(os.path.join(dpath, files)).Reload()]
+
+    tags = summary_iterators[0].Tags()['scalars']
+
+    for it in summary_iterators:
+        assert it.Tags()['scalars'] == tags
+
+    out = {t: [] for t in tags}
+    steps = []
+
+    for tag in tags:
+        steps = [e.step for e in summary_iterators[0].Scalars(tag)]
+
+        for events in zip(*[acc.Scalars(tag) for acc in summary_iterators]):
+            assert len(set(e.step for e in events)) == 1
+
+            out[tag].append([e.value for e in events])
+    
+    return out, steps
+
+events, steps = tabulate_events('/home/vedant/pytorch_sac_ae/log/runs')
+
+data = []
+
+for tag, values in events.items():
+    for run_idx, run_values in enumerate(values):
+        for step_idx, value in enumerate(run_values):
+            data.append({
+                'tag': tag,
+                'run': run_idx,
+                'step': steps[step_idx],
+                'value': value,
+            })
+
+df = pd.DataFrame(data)
+print(df.head())
+
+plt.figure(figsize=(10,6))
+sns.lineplot(data=df, x='step', y='value', hue='tag', ci='sd')
+plt.show()

sac_ae.py

@@ -6,7 +6,7 @@ import copy
 import math
 
 import utils
-from encoder import make_encoder
+from encoder import make_encoder, club_loss, TransitionModel
 from decoder import make_decoder
 
 LOG_FREQ = 10000
@@ -73,7 +73,7 @@ class Actor(nn.Module):
     def forward(
         self, obs, compute_pi=True, compute_log_pi=True, detach_encoder=False
     ):
-        obs = self.encoder(obs, detach=detach_encoder)
+        obs,_,_ = self.encoder(obs, detach=detach_encoder)
 
         mu, log_std = self.trunk(obs).chunk(2, dim=-1)
 
@@ -159,7 +159,7 @@ class Critic(nn.Module):
 
     def forward(self, obs, action, detach_encoder=False):
         # detach_encoder allows to stop gradient propogation to encoder
-        obs = self.encoder(obs, detach=detach_encoder)
+        obs,_,_ = self.encoder(obs, detach=detach_encoder)
 
         q1 = self.Q1(obs, action)
         q2 = self.Q2(obs, action)
@@ -182,6 +182,26 @@ class Critic(nn.Module):
             L.log_param('train_critic/q1_fc%d' % i, self.Q1.trunk[i * 2], step)
             L.log_param('train_critic/q2_fc%d' % i, self.Q2.trunk[i * 2], step)
 
+class LBLoss(nn.Module):
+    def __init__(self, z_dim):
+        super(LBLoss, self).__init__()
+        self.z_dim = z_dim
+
+        self.W = nn.Parameter(torch.rand(z_dim, z_dim))
+
+    def compute_logits(self, z_a, z_pos):
+        """
+        Uses logits trick for CURL:
+        - compute (B,B) matrix z_a (W z_pos.T)
+        - positives are all diagonal elements
+        - negatives are all other elements
+        - to compute loss use multiclass cross entropy with identity matrix for labels
+        """
+        Wz = torch.matmul(self.W, z_pos.T)  # (z_dim,B)
+        logits = torch.matmul(z_a, Wz)  # (B,B)
+        logits = logits - torch.max(logits, 1)[0][:, None]
+        return logits
+
 
 class SacAeAgent(object):
     """SAC+AE algorithm."""
@@ -240,7 +260,13 @@ class SacAeAgent(object):
             obs_shape, action_shape, hidden_dim, encoder_type,
             encoder_feature_dim, num_layers, num_filters
         ).to(device)
+        
+        self.transition_model = TransitionModel(
+            encoder_feature_dim, hidden_dim,  action_shape[0], history_size=256
+        ).to(device)
 
+        self.lb_loss = LBLoss(encoder_feature_dim).to(device)
+        
         self.critic_target.load_state_dict(self.critic.state_dict())
 
         # tie encoders between actor and critic
@@ -262,7 +288,10 @@ class SacAeAgent(object):
 
             # optimizer for critic encoder for reconstruction loss
             self.encoder_optimizer = torch.optim.Adam(
-                self.critic.encoder.parameters(), lr=encoder_lr
+                list(self.critic.encoder.parameters()) +
+                list(self.transition_model.parameters()), #+
+                #list(self.lb_loss.parameters()), 
+                lr=encoder_lr
             )
 
             # optimizer for decoder
@@ -366,32 +395,70 @@ class SacAeAgent(object):
         alpha_loss.backward()
         self.log_alpha_optimizer.step()
 
-    def update_decoder(self, obs, target_obs, L, step):
-        h = self.critic.encoder(obs)
+    def update_decoder(self, obs, target_obs, L, step, obs_list, action_list, reward_list, next_obs_list, not_done_list):
+        with torch.no_grad():
+            hist = torch.zeros((target_obs.shape[0], 256)).to(self.device)
+            for i in range(len(obs_list)-1):
+                state, _, _ = self.critic.encoder(obs_list[i])
+                action = action_list[i]
+                not_done = not_done_list[i]
+                state_enc = self.transition_model(state, action, hist, not_done)
+                hist = state_enc["history"]
+        
+        h, h_mu, h_logvar = self.critic.encoder(obs_list[-1])
+        h_clone = h.clone() 
+        
+        action = action_list[-1]
+        not_done = not_done_list[-1]
+        state_enc = self.transition_model(h, action, hist, not_done)
+        mean, std = state_enc["mean"], state_enc["logvar"].exp()
 
+        h_dist_enc = torch.distributions.Normal(h_mu, h_logvar.exp())
+        h_dist_pred = torch.distributions.Normal(mean, std)
+        enc_loss = torch.distributions.kl.kl_divergence(h_dist_enc, h_dist_pred).mean() * 1e-2
+
+        """
+        with torch.no_grad():
+            z_pos, _ , _ = self.critic_target.encoder(next_obs_list[-1])
+            z_out = self.critic_target.encoder.combine(torch.concat((z_pos, action), dim=-1))
+        logits = self.lb_loss.compute_logits(h, z_out)
+        labels = torch.arange(logits.shape[0]).long().to(self.device)
+        lb_loss = nn.CrossEntropyLoss()(logits, labels) * 1e-2
+        """
+        #with torch.no_grad():
+        #    z_pos, _ , _ = self.critic.encoder(next_obs_list[-1])
+        #ub_loss = club_loss(state_enc["sample"], mean, state_enc["logvar"], h) * 1e-1
+        
         if target_obs.dim() == 4:
             # preprocess images to be in [-0.5, 0.5] range
             target_obs = utils.preprocess_obs(target_obs)
-        rec_obs = self.decoder(h)
+        rec_obs = self.decoder(h_clone)
         rec_loss = F.mse_loss(target_obs, rec_obs)
 
-        # add L2 penalty on latent representation
-        # see https://arxiv.org/pdf/1903.12436.pdf
-        latent_loss = (0.5 * h.pow(2).sum(1)).mean()
-
-        loss = rec_loss + self.decoder_latent_lambda * latent_loss
+        ub_loss = torch.tensor(0.0)
+        #enc_loss = torch.tensor(0.0)
+        lb_loss = torch.tensor(0.0)
+        #rec_loss = torch.tensor(0.0)
+        loss = rec_loss + enc_loss + lb_loss + ub_loss
         self.encoder_optimizer.zero_grad()
         self.decoder_optimizer.zero_grad()
         loss.backward()
 
         self.encoder_optimizer.step()
         self.decoder_optimizer.step()
+
+        #enc_loss = torch.tensor(0.0)
         L.log('train_ae/ae_loss', loss, step)
+        L.log('train_ae/rec_loss', rec_loss, step)
+        L.log('train_ae/enc_loss', enc_loss, step)
+        L.log('train_ae/lb_loss', lb_loss, step)
+        L.log('train_ae/ub_loss', ub_loss, step)
 
         self.decoder.log(L, step, log_freq=LOG_FREQ)
 
     def update(self, replay_buffer, L, step):
-        obs, action, reward, next_obs, not_done = replay_buffer.sample()
+        obs_list, action_list, reward_list, next_obs_list, not_done_list = replay_buffer.sample()
+        obs, action, reward, next_obs, not_done = obs_list[-1], action_list[-1], reward_list[-1], next_obs_list[-1], not_done_list[-1]  
 
         L.log('train/batch_reward', reward.mean(), step)
 
@@ -413,7 +480,7 @@ class SacAeAgent(object):
             )
 
         if self.decoder is not None and step % self.decoder_update_freq == 0:
-            self.update_decoder(obs, obs, L, step)
+            self.update_decoder(obs, obs, L, step, obs_list, action_list, reward_list, next_obs_list, not_done_list)
 
     def save(self, model_dir, step):
         torch.save(

train.py

@@ -28,36 +28,40 @@ def parse_args():
     parser.add_argument('--frame_stack', default=3, type=int)
     parser.add_argument('--img_source', default=None, type=str, choices=['color', 'noise', 'images', 'video', 'none'])
     parser.add_argument('--resource_files', type=str)
+<<<<<<< HEAD
+    parser.add_argument('--resource_files_test', type=str)
+=======
+>>>>>>> origin/tester_1
     parser.add_argument('--total_frames', default=10000, type=int)
     # replay buffer
-    parser.add_argument('--replay_buffer_capacity', default=1000000, type=int)
+    parser.add_argument('--replay_buffer_capacity', default=100000, type=int)
     # train
     parser.add_argument('--agent', default='sac_ae', type=str)
     parser.add_argument('--init_steps', default=1000, type=int)
-    parser.add_argument('--num_train_steps', default=1000000, type=int)
-    parser.add_argument('--batch_size', default=128, type=int)
+    parser.add_argument('--num_train_steps', default=2000000, type=int)
+    parser.add_argument('--batch_size', default=32, type=int)
     parser.add_argument('--hidden_dim', default=1024, type=int)
     # eval
     parser.add_argument('--eval_freq', default=10000, type=int)
     parser.add_argument('--num_eval_episodes', default=10, type=int)
     # critic
-    parser.add_argument('--critic_lr', default=1e-3, type=float)
+    parser.add_argument('--critic_lr', default=1e-4, type=float)
     parser.add_argument('--critic_beta', default=0.9, type=float)
     parser.add_argument('--critic_tau', default=0.01, type=float)
     parser.add_argument('--critic_target_update_freq', default=2, type=int)
     # actor
-    parser.add_argument('--actor_lr', default=1e-3, type=float)
+    parser.add_argument('--actor_lr', default=1e-4, type=float)
     parser.add_argument('--actor_beta', default=0.9, type=float)
     parser.add_argument('--actor_log_std_min', default=-10, type=float)
     parser.add_argument('--actor_log_std_max', default=2, type=float)
     parser.add_argument('--actor_update_freq', default=2, type=int)
     # encoder/decoder
     parser.add_argument('--encoder_type', default='pixel', type=str)
-    parser.add_argument('--encoder_feature_dim', default=50, type=int)
-    parser.add_argument('--encoder_lr', default=1e-3, type=float)
+    parser.add_argument('--encoder_feature_dim', default=250, type=int)
+    parser.add_argument('--encoder_lr', default=1e-4, type=float)
     parser.add_argument('--encoder_tau', default=0.05, type=float)
     parser.add_argument('--decoder_type', default='pixel', type=str)
-    parser.add_argument('--decoder_lr', default=1e-3, type=float)
+    parser.add_argument('--decoder_lr', default=1e-4, type=float)
     parser.add_argument('--decoder_update_freq', default=1, type=int)
     parser.add_argument('--decoder_latent_lambda', default=1e-6, type=float)
     parser.add_argument('--decoder_weight_lambda', default=1e-7, type=float)
@@ -153,9 +157,25 @@ def main():
     )
     env.seed(args.seed)
 
+    env_test = dmc2gym.make(
+        domain_name=args.domain_name,
+        task_name=args.task_name,
+        seed=args.seed,
+        visualize_reward=False,
+        from_pixels=(args.encoder_type == 'pixel'),
+        height=args.image_size,
+        width=args.image_size,
+        frame_skip=args.action_repeat,
+        img_source=args.img_source,
+        resource_files=args.resource_files_test,
+        total_frames=args.total_frames
+    )
+    env_test.seed(args.seed)
+
     # stack several consecutive frames together
     if args.encoder_type == 'pixel':
         env = utils.FrameStack(env, k=args.frame_stack)
+        env_test = utils.FrameStack(env_test, k=args.frame_stack)
 
     utils.make_dir(args.work_dir)
     video_dir = utils.make_dir(os.path.join(args.work_dir, 'video'))
@@ -202,7 +222,7 @@ def main():
             # evaluate agent periodically
             if step % args.eval_freq == 0:
                 L.log('eval/episode', episode, step)
-                evaluate(env, agent, video, args.num_eval_episodes, L, step)
+                evaluate(env_test, agent, video, args.num_eval_episodes, L, step)
                 if args.save_model:
                     agent.save(model_dir, step)
                 if args.save_buffer:

utils.py

@@ -96,17 +96,17 @@ class ReplayBuffer(object):
         self.full = self.full or self.idx == 0
 
     def sample(self):
+        begin = 2
         idxs = np.random.randint(
-            0, self.capacity if self.full else self.idx, size=self.batch_size
+            begin, self.capacity if self.full else self.idx, size=self.batch_size
         )
+        past_idxs = idxs - begin
 
-        obses = torch.as_tensor(self.obses[idxs], device=self.device).float()
-        actions = torch.as_tensor(self.actions[idxs], device=self.device)
-        rewards = torch.as_tensor(self.rewards[idxs], device=self.device)
-        next_obses = torch.as_tensor(
-            self.next_obses[idxs], device=self.device
-        ).float()
-        not_dones = torch.as_tensor(self.not_dones[idxs], device=self.device)
+        obses = torch.as_tensor(np.swapaxes(np.asarray([self.obses[past_idxs:idxs] for past_idxs, idxs in zip(past_idxs, idxs)]),0,1), device=self.device).float()
+        actions = torch.as_tensor(np.swapaxes(np.asarray([self.actions[past_idxs:idxs] for past_idxs, idxs in zip(past_idxs, idxs)]),0,1), device=self.device)
+        rewards = torch.as_tensor(np.swapaxes(np.asarray([self.rewards[past_idxs:idxs] for past_idxs, idxs in zip(past_idxs, idxs)]),0,1), device=self.device)
+        next_obses = torch.as_tensor(np.swapaxes(np.asarray([self.next_obses[past_idxs:idxs] for past_idxs, idxs in zip(past_idxs, idxs)]),0,1), device=self.device).float()
+        not_dones = torch.as_tensor(np.swapaxes(np.asarray([self.not_dones[past_idxs:idxs] for past_idxs, idxs in zip(past_idxs, idxs)]),0,1), device=self.device)
 
         return obses, actions, rewards, next_obses, not_dones