Adding new background videos for each episode

DPI/models.py

@@ -131,12 +131,16 @@ class CLUBSample(nn.Module):  # Sampled version of the CLUB estimator
         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()
                         )
@@ -183,4 +187,3 @@ if __name__ == "__main__":
     print(x_enc.shape)
     print(y_enc.shape)
     print(club.learning_loss(x_enc, y_enc))
-

DPI/train.py

@@ -169,20 +169,26 @@ class DPI:
         done = False
 
         for episode_count in range(episodes):
+            video = VideoRecorder(self.video_dir if args.save_video else None, resource_files=args.resource_files)
+            video.init(enabled=True)
             for i in range(self.args.episode_length):
                 action = self.env.action_space.sample()
                 next_obs, _, done, _ = self.env.step(action)
 
                 self.data_buffer.add(obs, action, next_obs, episode_count+1, done)
                 
+                if args.save_video:
+                    video.record(self.env)
+                
                 if done:
                     obs = self.env.reset()
                     done=False  
                 else:
                     obs = next_obs
+            video.save('%d.mp4' % episode_count)
         print("Collected {} random episodes".format(episode_count+1))
                 #if args.save_video:
-                #    video.record(env)
+                #    video.record(self.env)
         #video.save('%d.mp4' % step)     
         #video.close()
 
@@ -195,13 +201,16 @@ class DPI:
         next_observations = torch.Tensor(self.data_buffer.group_steps(self.data_buffer,"next_observations")).float()
         
         # Train encoder
+        previous_information_loss = 0
         for i in range(self.args.episode_length):
             # Encode observations and next_observations
             self.features = self.obs_encoder(observations[i]) # (N,128)
             self.next_features = self.obs_encoder(next_observations[i]) # (N,128)
 
             # Calculate upper bound loss
-            past_loss = self.upper_bound_minimization(self.features, self.next_features)
+            past_loss = previous_information_loss + self.upper_bound_minimization(self.features, self.next_features)
+            previous_information_loss = past_loss
+            print("past_loss: ", past_loss)
         
     def upper_bound_minimization(self, features, next_features):
         club_sample = CLUBSample(self.args.state_size, 

DPI/utils.py

@@ -1,3 +1,9 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
 import os
 import torch
 import numpy as np
@@ -166,6 +172,9 @@ class ReplayBuffer:
     
 
 def make_env(args):
+    # For making ground plane transparent, change rgba to (0, 0, 0, 0) in local_dm_control_suite/{domain_name}.xml, 
+    # else change to (0.5, 0.5, 0.5, 1.0) for default ground plane color
+    # https://mujoco.readthedocs.io/en/stable/XMLreference.html#body-geom
     env = dmc2gym.make(
             domain_name=args.domain_name,
             task_name=args.task_name,

DPI/video.py

@@ -1,3 +1,9 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
 import imageio
 import os
 import numpy as np