LICENSE

@@ -1,21 +0,0 @@
-MIT License
-
-Copyright (c) 2020 Denis Yarats
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.

README.md

@@ -8,18 +8,6 @@ This is PyTorch implementation of SAC+AE from
 
 [[Paper]](https://arxiv.org/abs/1910.01741) [[Webpage]](https://sites.google.com/view/sac-ae/home)
 
-## Citation
-If you use this repo in your research, please consider citing the paper as follows
-```
-@article{yarats2019improving,
-    title={Improving Sample Efficiency in Model-Free Reinforcement Learning from Images},
-    author={Denis Yarats and Amy Zhang and Ilya Kostrikov and Brandon Amos and Joelle Pineau and Rob Fergus},
-    year={2019},
-    eprint={1910.01741},
-    archivePrefix={arXiv}
-}
-```
-
 ## Requirements
 We assume you have access to a gpu that can run CUDA 9.2. Then, the simplest way to install all required dependencies is to create an anaconda environment by running:
 ```

conda_env.yml

@@ -11,7 +11,7 @@ dependencies:
   - pip:
     - termcolor
     - git+git://github.com/deepmind/dm_control.git
-    - git+git://github.com/denisyarats/dmc2gym.git
+    - git+git://github.com/1nadequacy/dmc2gym.git
     - tb-nightly
     - imageio
     - imageio-ffmpeg

train.py

@@ -26,16 +26,13 @@ def parse_args():
     parser.add_argument('--image_size', default=84, type=int)
     parser.add_argument('--action_repeat', default=1, type=int)
     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)
-    parser.add_argument('--total_frames', default=10000, type=int)
     # replay buffer
     parser.add_argument('--replay_buffer_capacity', default=1000000, 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=512, type=int)
+    parser.add_argument('--batch_size', default=128, type=int)
     parser.add_argument('--hidden_dim', default=1024, type=int)
     # eval
     parser.add_argument('--eval_freq', default=10000, type=int)
@@ -146,10 +143,7 @@ def main():
         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,
-        total_frames=args.total_frames
+        frame_skip=args.action_repeat
     )
     env.seed(args.seed)
 
@@ -218,65 +212,28 @@ def main():
 
             L.log('train/episode', episode, step)
 
-        if episode_step == 0:
-            last_obs = obs
-            # sample action for data collection
-            if step < args.init_steps:
-                last_action = env.action_space.sample()
-            else:
-                with utils.eval_mode(agent):
-                    last_action = agent.sample_action(last_obs)
-            
-            curr_obs, last_reward, last_done, _ = env.step(last_action)
-
-            # allow infinit bootstrap
-            last_done_bool = 0 if episode_step + 1 == env._max_episode_steps else float(last_done)
-            episode_reward += last_reward
-
         # sample action for data collection
         if step < args.init_steps:
             action = env.action_space.sample()
         else:
             with utils.eval_mode(agent):
-                    action = agent.sample_action(curr_obs)
-            
-            next_obs, reward, done, _ = env.step(action)
-
-            # allow infinit bootstrap
-            done_bool = 0 if episode_step + 1 == env._max_episode_steps else float(done)
-            episode_reward += reward
-
-            replay_buffer.add(last_obs, last_action, last_reward, curr_obs, last_done_bool, action, reward, next_obs, done_bool)
-
-        last_obs = curr_obs
-        last_action = action
-        last_reward = reward
-        last_done = done
-        curr_obs = next_obs
-
-        # sample action for data collection
-        if step < args.init_steps:
-            action = env.action_space.sample()
-        else:
-            with utils.eval_mode(agent):
-                action = agent.sample_action(curr_obs)
-
+                action = agent.sample_action(obs)
 
         # run training update
         if step >= args.init_steps:
-            #num_updates = args.init_steps if step == args.init_steps else 1
-            num_updates = 1 if step == args.init_steps else 1
+            num_updates = args.init_steps if step == args.init_steps else 1
             for _ in range(num_updates):
                 agent.update(replay_buffer, L, step)
 
         next_obs, reward, done, _ = env.step(action)
 
         # allow infinit bootstrap
-        done_bool = 0 if episode_step + 1 == env._max_episode_steps else float(done)
+        done_bool = 0 if episode_step + 1 == env._max_episode_steps else float(
+            done
+        )
         episode_reward += reward
 
-        #replay_buffer.add(obs, action, reward, next_obs, done_bool)
-        replay_buffer.add(last_obs, last_action, last_reward, curr_obs, last_done_bool, action, reward, next_obs, done_bool)
+        replay_buffer.add(obs, action, reward, next_obs, done_bool)
 
         obs = next_obs
         episode_step += 1