DQN supports gym as well.

examples/DeepQNetwork/DQN.py

@@ -8,18 +8,19 @@ import argparse
 import cv2
 import numpy as np
 import tensorflow as tf
-
+import gym
 
 from tensorpack import *
 
 from DQNModel import Model as DQNModel
 from common import Evaluator, eval_model_multithread, play_n_episodes
-from atari_wrapper import FrameStack, MapState, FireResetEnv
+from atari_wrapper import FrameStack, MapState, FireResetEnv, LimitLength
 from expreplay import ExpReplay
 from atari import AtariPlayer
 
 BATCH_SIZE = 64
 IMAGE_SIZE = (84, 84)
+IMAGE_CHANNEL = None  # 3 in gym and 1 in our own wrapper
 FRAME_HISTORY = 4
 ACTION_REPEAT = 4   # aka FRAME_SKIP
 UPDATE_FREQ = 4
@@ -33,24 +34,39 @@ STEPS_PER_EPOCH = 100000 // UPDATE_FREQ  # each epoch is 100k played frames
 EVAL_EPISODE = 50
 
 NUM_ACTIONS = None
-ROM_FILE = None
+USE_GYM = False
+ENV_NAME = None
 METHOD = None
 
 
+def resize_keepdims(im, size):
+    # Opencv's resize remove the extra dimension for grayscale images.
+    # We add it back.
+    ret = cv2.resize(im, size)
+    if im.ndim == 3 and ret.ndim == 2:
+        ret = ret[:, :, np.newaxis]
+    return ret
+
+
 def get_player(viz=False, train=False):
-    env = AtariPlayer(ROM_FILE, frame_skip=ACTION_REPEAT, viz=viz,
-                      live_lost_as_eoe=train, max_num_frames=60000)
+    if USE_GYM:
+        env = gym.make(ENV_NAME)
+    else:
+        env = AtariPlayer(ENV_NAME, frame_skip=ACTION_REPEAT, viz=viz,
+                          live_lost_as_eoe=train, max_num_frames=60000)
     env = FireResetEnv(env)
-    env = MapState(env, lambda im: cv2.resize(im, IMAGE_SIZE)[:, :, np.newaxis])
+    env = MapState(env, lambda im: resize_keepdims(im, IMAGE_SIZE))
     if not train:
         # in training, history is taken care of in expreplay buffer
         env = FrameStack(env, FRAME_HISTORY)
+    if train and USE_GYM:
+        env = LimitLength(env, 60000)
     return env
 
 
 class Model(DQNModel):
     def __init__(self):
-        super(Model, self).__init__(IMAGE_SIZE, 1, FRAME_HISTORY, METHOD, NUM_ACTIONS, GAMMA)
+        super(Model, self).__init__(IMAGE_SIZE, IMAGE_CHANNEL, FRAME_HISTORY, METHOD, NUM_ACTIONS, GAMMA)
 
     def _get_DQN_prediction(self, image):
         image = image / 255.0
@@ -86,7 +102,7 @@ def get_config():
     expreplay = ExpReplay(
         predictor_io_names=(['state'], ['Qvalue']),
         player=get_player(train=True),
-        state_shape=IMAGE_SIZE + (1,),
+        state_shape=IMAGE_SIZE + (IMAGE_CHANNEL,),
         batch_size=BATCH_SIZE,
         memory_size=MEMORY_SIZE,
         init_memory_size=INIT_MEMORY_SIZE,
@@ -126,18 +142,21 @@ if __name__ == '__main__':
     parser.add_argument('--load', help='load model')
     parser.add_argument('--task', help='task to perform',
                         choices=['play', 'eval', 'train'], default='train')
-    parser.add_argument('--rom', help='atari rom', required=True)
+    parser.add_argument('--env', required=True,
+                        help='either an atari rom file (that ends with .bin) or a gym atari environment name')
     parser.add_argument('--algo', help='algorithm',
                         choices=['DQN', 'Double', 'Dueling'], default='Double')
     args = parser.parse_args()
 
     if args.gpu:
         os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
-    ROM_FILE = args.rom
+    ENV_NAME = args.env
+    USE_GYM = not ENV_NAME.endswith('.bin')
+    IMAGE_CHANNEL = 3 if USE_GYM else 1
     METHOD = args.algo
     # set num_actions
-    NUM_ACTIONS = AtariPlayer(ROM_FILE).action_space.n
-    logger.info("ROM: {}, Num Actions: {}".format(ROM_FILE, NUM_ACTIONS))
+    NUM_ACTIONS = get_player().action_space.n
+    logger.info("ENV: {}, Num Actions: {}".format(ENV_NAME, NUM_ACTIONS))
 
     if args.task != 'train':
         assert args.load is not None
@@ -153,7 +172,7 @@ if __name__ == '__main__':
     else:
         logger.set_logger_dir(
             os.path.join('train_log', 'DQN-{}'.format(
-                os.path.basename(ROM_FILE).split('.')[0])))
+                os.path.basename(ENV_NAME).split('.')[0])))
         config = get_config()
         if args.load:
             config.session_init = get_model_loader(args.load)

examples/DeepQNetwork/README.md

@@ -26,6 +26,7 @@ Double-DQN with nature paper setting runs at 60 batches (3840 trained frames, 24
 
 ## How to use
 
+### With ALE (paper's setting):
 Install [ALE](https://github.com/mgbellemare/Arcade-Learning-Environment) and gym.
 
 Download an [atari rom](https://github.com/openai/atari-py/tree/master/atari_py/atari_roms), e.g.:
@@ -35,7 +36,7 @@ wget https://github.com/openai/atari-py/raw/master/atari_py/atari_roms/breakout.
 
 Start Training:
 ```
-./DQN.py --rom breakout.bin
+./DQN.py --env breakout.bin
 # use `--algo` to select other DQN algorithms. See `-h` for more options.
 ```
 
@@ -43,7 +44,15 @@ Watch the agent play:
 ```
 # Download pretrained models or use one you trained:
 wget http://models.tensorpack.com/DeepQNetwork/DoubleDQN-Breakout.npz
-./DQN.py --rom breakout.bin --task play --load DoubleDQN-Breakout.npz
+./DQN.py --env breakout.bin --task play --load DoubleDQN-Breakout.npz
+```
+
+### With gym's Atari:
+
+Install gym and atari_py.
+
+```
+./DQN.py --env BreakoutDeterministic-v4
 ```
 
 A3C code and models for Atari games in OpenAI Gym are released in [examples/A3C-Gym](../A3C-Gym)

examples/DeepQNetwork/atari.py

@@ -95,7 +95,7 @@ class AtariPlayer(gym.Env):
 
         self.action_space = spaces.Discrete(len(self.actions))
         self.observation_space = spaces.Box(
-            low=0, high=255, shape=(self.height, self.width), dtype=np.uint8)
+            low=0, high=255, shape=(self.height, self.width, 1), dtype=np.uint8)
         self._restart_episode()
 
     def get_action_meanings(self):
@@ -110,7 +110,7 @@ class AtariPlayer(gym.Env):
 
     def _current_state(self):
         """
-        :returns: a gray-scale (h, w) uint8 image
+        :returns: a gray-scale (h, w, 1) uint8 image
         """
         ret = self._grab_raw_image()
         # max-pooled over the last screen
@@ -121,7 +121,7 @@ class AtariPlayer(gym.Env):
                 cv2.waitKey(int(self.viz * 1000))
         ret = ret.astype('float32')
         # 0.299,0.587.0.114. same as rgb2y in torch/image
-        ret = cv2.cvtColor(ret, cv2.COLOR_RGB2GRAY)
+        ret = cv2.cvtColor(ret, cv2.COLOR_RGB2GRAY)[:, :, np.newaxis]
         return ret.astype('uint8')  # to save some memory
 
     def _restart_episode(self):

examples/DeepQNetwork/expreplay.py

@@ -135,7 +135,7 @@ class ExpReplay(DataFlow, Callback):
         Args:
             predictor_io_names (tuple of list of str): input/output names to
                 predict Q value from state.
-            player (RLEnvironment): the player.
+            player (gym.Env): the player.
             state_shape (tuple): h, w, c
             history_len (int): length of history frames to concat. Zero-filled
                 initial frames.

examples/DoReFa-Net/dorefa.py

@@ -30,7 +30,7 @@ def get_dorefa(bitW, bitA, bitG):
 
             @tf.custom_gradient
             def _sign(x):
-                return tf.sign(x / E) * E, lambda dy: dy
+                return tf.where(tf.equal(x, 0), tf.ones_like(x), tf.sign(x / E)) * E, lambda dy: dy
 
             return _sign(x)