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[rllib] Switch DQN to using deepmind wrappers (#1655)

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* deepmind wrap

* use 80x80

* respect custom prep

* fix replay size

* fix chekc

* batch idx

* Wed Mar  7 11:00:39 PST 2018

* random starts and reward clipping

* Fri Mar  9 17:27:17 PST 2018

* Fri Mar  9 17:36:15 PST 2018

* Sat Mar 10 19:47:10 PST 2018

* Sat Mar 10 19:47:37 PST 2018

* Sat Mar 10 20:05:12 PST 2018

* Sat Mar 10 20:54:21 PST 2018

* Sat Mar 10 21:03:52 PST 2018
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Eric Liang 2018-03-11 21:14:38 -07:00 • committed by GitHub
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202
python/ray/rllib/dqn/common/atari_wrappers.py Normal file
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@ -0,0 +1,202 @@
import numpy as np
from collections import deque
import gym
from gym import spaces
import cv2
cv2.ocl.setUseOpenCL(False)
class NoopResetEnv(gym.Wrapper):
def __init__(self, env, noop_max=30, random_starts=False):
"""Sample initial states by taking random number of no-ops on reset.
No-op is assumed to be action 0.
"""
gym.Wrapper.__init__(self, env)
self.noop_max = noop_max
self.override_num_noops = None
self.noop_action = 0
self.random_starts = random_starts
assert env.unwrapped.get_action_meanings()[0] == 'NOOP'
def reset(self, **kwargs):
""" Do no-op action for a number of steps in [1, noop_max]."""
self.env.reset(**kwargs)
if self.override_num_noops is not None:
noops = self.override_num_noops
else:
noops = self.unwrapped.np_random.randint(
1, self.noop_max + 1)
assert noops > 0
obs = None
for _ in range(noops):
if self.random_starts:
action = np.random.randint(self.env.action_space.n)
else:
action = self.noop_action
obs, _, done, _ = self.env.step(action)
if done:
obs = self.env.reset(**kwargs)
return obs
def step(self, ac):
return self.env.step(ac)
class ClipRewardEnv(gym.RewardWrapper):
def __init__(self, env):
gym.RewardWrapper.__init__(self, env)
def reward(self, reward):
"""Bin reward to {+1, 0, -1} by its sign."""
return np.sign(reward)
class FireResetEnv(gym.Wrapper):
def __init__(self, env):
"""Take action on reset.
For environments that are fixed until firing."""
gym.Wrapper.__init__(self, env)
assert env.unwrapped.get_action_meanings()[1] == 'FIRE'
assert len(env.unwrapped.get_action_meanings()) >= 3
def reset(self, **kwargs):
self.env.reset(**kwargs)
obs, _, done, _ = self.env.step(1)
if done:
self.env.reset(**kwargs)
obs, _, done, _ = self.env.step(2)
if done:
self.env.reset(**kwargs)
return obs
def step(self, ac):
return self.env.step(ac)
class EpisodicLifeEnv(gym.Wrapper):
def __init__(self, env):
"""Make end-of-life == end-of-episode, but only reset on true game over.
Done by DeepMind for the DQN and co. since it helps value estimation.
"""
gym.Wrapper.__init__(self, env)
self.lives = 0
self.was_real_done = True
def step(self, action):
obs, reward, done, info = self.env.step(action)
self.was_real_done = done
# check current lives, make loss of life terminal,
# then update lives to handle bonus lives
lives = self.env.unwrapped.ale.lives()
if lives < self.lives and lives > 0:
# for Qbert sometimes we stay in lives == 0 condtion for a few
# frames so its important to keep lives > 0, so that we only reset
# once the environment advertises done.
done = True
self.lives = lives
return obs, reward, done, info
def reset(self, **kwargs):
"""Reset only when lives are exhausted.
This way all states are still reachable even though lives are episodic,
and the learner need not know about any of this behind-the-scenes.
"""
if self.was_real_done:
obs = self.env.reset(**kwargs)
else:
# no-op step to advance from terminal/lost life state
obs, _, _, _ = self.env.step(0)
self.lives = self.env.unwrapped.ale.lives()
return obs
class MaxAndSkipEnv(gym.Wrapper):
def __init__(self, env, skip=4):
"""Return only every `skip`-th frame"""
gym.Wrapper.__init__(self, env)
# most recent raw observations (for max pooling across time steps)
self._obs_buffer = np.zeros(
(2,)+env.observation_space.shape, dtype=np.uint8)
self._skip = skip
def step(self, action):
"""Repeat action, sum reward, and max over last observations."""
total_reward = 0.0
done = None
for i in range(self._skip):
obs, reward, done, info = self.env.step(action)
if i == self._skip - 2:
self._obs_buffer[0] = obs
if i == self._skip - 1:
self._obs_buffer[1] = obs
total_reward += reward
if done:
break
# Note that the observation on the done=True frame
# doesn't matter
max_frame = self._obs_buffer.max(axis=0)
return max_frame, total_reward, done, info
def reset(self, **kwargs):
return self.env.reset(**kwargs)
class WarpFrame(gym.ObservationWrapper):
def __init__(self, env):
"""Warp frames to 84x84 as done in the Nature paper and later work."""
gym.ObservationWrapper.__init__(self, env)
self.width = 80 # in rllib we use 80
self.height = 80
self.observation_space = spaces.Box(
low=0, high=255, shape=(self.height, self.width, 1))
def observation(self, frame):
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
frame = cv2.resize(
frame, (self.width, self.height), interpolation=cv2.INTER_AREA)
return frame[:, :, None]
class FrameStack(gym.Wrapper):
def __init__(self, env, k):
"""Stack k last frames."""
gym.Wrapper.__init__(self, env)
self.k = k
self.frames = deque([], maxlen=k)
shp = env.observation_space.shape
self.observation_space = spaces.Box(
low=0, high=255, shape=(shp[0], shp[1], shp[2] * k))
def reset(self):
ob = self.env.reset()
for _ in range(self.k):
self.frames.append(ob)
return self._get_ob()
def step(self, action):
ob, reward, done, info = self.env.step(action)
self.frames.append(ob)
return self._get_ob(), reward, done, info
def _get_ob(self):
assert len(self.frames) == self.k
return np.concatenate(self.frames, axis=2)
def wrap_deepmind(env, random_starts):
"""Configure environment for DeepMind-style Atari.
Note that we assume reward clipping is done outside the wrapper.
"""
env = NoopResetEnv(env, noop_max=30, random_starts=random_starts)
if 'NoFrameskip' in env.spec.id:
env = MaxAndSkipEnv(env, skip=4)
env = EpisodicLifeEnv(env)
if 'FIRE' in env.unwrapped.get_action_meanings():
env = FireResetEnv(env)
env = WarpFrame(env)
# env = ClipRewardEnv(env) # reward clipping is handled by DQN replay
env = FrameStack(env, 4)
return env

232
python/ray/rllib/dqn/common/wrappers.py
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@ -2,236 +2,18 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import cv2
import gym
import numpy as np
from collections import deque
from gym import spaces
from ray.rllib.models import ModelCatalog
from ray.rllib.dqn.common.atari_wrappers import wrap_deepmind
class NoopResetEnv(gym.Wrapper):
def __init__(self, env=None, noop_max=30):
"""Sample initial states by taking random number of no-ops on reset.
No-op is assumed to be action 0.
"""
super(NoopResetEnv, self).__init__(env)
self.noop_max = noop_max
self.override_num_noops = None
assert env.unwrapped.get_action_meanings()[0] == 'NOOP'
def reset(self):
""" Do no-op action for a number of steps in [1, noop_max]."""
self.env.reset()
if self.override_num_noops is not None:
noops = self.override_num_noops
else:
noops = np.random.randint(1, self.noop_max + 1)
assert noops > 0
obs = None
for _ in range(noops):
obs, _, done, _ = self.env.step(0)
if done:
obs = self.env.reset()
return obs
class FireResetEnv(gym.Wrapper):
def __init__(self, env=None):
"""For environments where the user need to press FIRE for the game to
start."""
super(FireResetEnv, self).__init__(env)
assert env.unwrapped.get_action_meanings()[1] == 'FIRE'
assert len(env.unwrapped.get_action_meanings()) >= 3
def reset(self):
self.env.reset()
obs, _, done, _ = self.env.step(1)
if done:
self.env.reset()
obs, _, done, _ = self.env.step(2)
if done:
self.env.reset()
return obs
class EpisodicLifeEnv(gym.Wrapper):
def __init__(self, env=None):
"""Make end-of-life == end-of-episode, but only reset on true game
over. Done by DeepMind for the DQN and co. since it helps value
estimation.
"""
super(EpisodicLifeEnv, self).__init__(env)
self.lives = 0
self.was_real_done = True
self.was_real_reset = False
def step(self, action):
obs, reward, done, info = self.env.step(action)
self.was_real_done = done
# check current lives, make loss of life terminal,
# then update lives to handle bonus lives
lives = self.env.unwrapped.ale.lives()
if lives < self.lives and lives > 0:
# for Qbert somtimes we stay in lives == 0 condtion for a few
# frames so its important to keep lives > 0, so that we only reset
# once the environment advertises done.
done = True
self.lives = lives
return obs, reward, done, info
def reset(self):
"""Reset only when lives are exhausted.
This way all states are still reachable even though lives are episodic,
and the learner need not know about any of this behind-the-scenes.
"""
if self.was_real_done:
obs = self.env.reset()
self.was_real_reset = True
else:
# no-op step to advance from terminal/lost life state
obs, _, _, _ = self.env.step(0)
self.was_real_reset = False
self.lives = self.env.unwrapped.ale.lives()
return obs
class MaxAndSkipEnv(gym.Wrapper):
def __init__(self, env=None, skip=4):
"""Return only every `skip`-th frame"""
super(MaxAndSkipEnv, self).__init__(env)
# most recent raw observations (for max pooling across time steps)
self._obs_buffer = deque(maxlen=2)
self._skip = skip
def step(self, action):
total_reward = 0.0
done = None
for _ in range(self._skip):
obs, reward, done, info = self.env.step(action)
self._obs_buffer.append(obs)
total_reward += reward
if done:
break
max_frame = np.max(np.stack(self._obs_buffer), axis=0)
return max_frame, total_reward, done, info
def reset(self):
"""Clear past frame buffer and init. to first obs. from inner env."""
self._obs_buffer.clear()
obs = self.env.reset()
self._obs_buffer.append(obs)
return obs
# TODO(ekl): switch this to use a RLlib common preprocessor
class ProcessFrame80(gym.ObservationWrapper):
def __init__(self, env=None):
super(ProcessFrame80, self).__init__(env)
self.observation_space = spaces.Box(
low=0, high=255, shape=(80, 80, 1), dtype=np.uint8)
def observation(self, obs):
return ProcessFrame80.process(obs)
@staticmethod
def process(frame):
if frame.size == 210 * 160 * 3:
img = np.reshape(frame, [210, 160, 3]).astype(np.float32)
elif frame.size == 250 * 160 * 3:
img = np.reshape(frame, [250, 160, 3]).astype(np.float32)
else:
assert False, "Unknown resolution."
img = (img[:, :, 0] * 0.299 + img[:, :, 1] * 0.587 +
img[:, :, 2] * 0.114)
resized_screen = cv2.resize(
img, (80, 110), interpolation=cv2.INTER_AREA)
x_t = resized_screen[20:100, :]
x_t = np.reshape(x_t, [80, 80, 1])
return x_t.astype(np.uint8)
class ClippedRewardsWrapper(gym.RewardWrapper):
def reward(self, reward):
"""Change all the positive rewards to 1, negative to -1 and keep
zero."""
return np.sign(reward)
class LazyFrames(object):
def __init__(self, frames):
"""This object ensures that common frames between the observations are
only stored once. It exists purely to optimize memory usage which can
be huge for DQN's 1M frames replay buffers.
This object should only be converted to numpy array before being passed
to the model.
You'd not belive how complex the previous solution was."""
self._frames = frames
def __array__(self, dtype=None):
out = np.concatenate(self._frames, axis=2)
if dtype is not None:
out = out.astype(dtype)
return out
class FrameStack(gym.Wrapper):
def __init__(self, env, k):
"""Stack k last frames.
Returns lazy array, which is much more memory efficient.
See Also
--------
LazyFrames
"""
gym.Wrapper.__init__(self, env)
self.k = k
self.frames = deque([], maxlen=k)
shp = env.observation_space.shape
self.observation_space = spaces.Box(
low=0, high=255, shape=(shp[0], shp[1], shp[2] * k),
dtype=np.uint8)
def reset(self):
ob = self.env.reset()
for _ in range(self.k):
self.frames.append(ob)
return self._get_ob()
def step(self, action):
ob, reward, done, info = self.env.step(action)
self.frames.append(ob)
return self._get_ob(), reward, done, info
def _get_ob(self):
assert len(self.frames) == self.k
return LazyFrames(list(self.frames))
def wrap_dqn(registry, env, options):
def wrap_dqn(registry, env, options, random_starts):
"""Apply a common set of wrappers for DQN."""
is_atari = hasattr(env.unwrapped, "ale")
if is_atari:
env = EpisodicLifeEnv(env)
env = NoopResetEnv(env, noop_max=30)
if 'NoFrameskip' in env.spec.id:
env = MaxAndSkipEnv(env, skip=4)
if 'FIRE' in env.unwrapped.get_action_meanings():
env = FireResetEnv(env)
# Override atari default to use the deepmind wrappers.
# TODO(ekl) this logic should be pushed to the catalog.
if is_atari and "custom_preprocessor" not in options:
return wrap_deepmind(env, random_starts=random_starts)
env = ModelCatalog.get_preprocessor_as_wrapper(registry, env, options)
if is_atari:
env = FrameStack(env, 4)
env = ClippedRewardsWrapper(env)
return env
return ModelCatalog.get_preprocessor_as_wrapper(registry, env, options)

4
python/ray/rllib/dqn/dqn.py
View file

@ -52,6 +52,8 @@ DEFAULT_CONFIG = dict(
exploration_final_eps=0.02,
# Update the target network every `target_network_update_freq` steps.
target_network_update_freq=500,
# Whether to start with random actions instead of noops.
random_starts=True,
# === Replay buffer ===
# Size of the replay buffer. Note that if async_updates is set, then
@ -65,6 +67,8 @@ DEFAULT_CONFIG = dict(
prioritized_replay_beta=0.4,
# Epsilon to add to the TD errors when updating priorities.
prioritized_replay_eps=1e-6,
# Whether to clip rewards to [-1, 1] prior to adding to the replay buffer.
clip_rewards=True,
# === Optimization ===
# Learning rate for adam optimizer

2
python/ray/rllib/dqn/dqn_evaluator.py
View file

@ -50,7 +50,7 @@ class DQNEvaluator(Evaluator):
def __init__(self, registry, env_creator, config, logdir, worker_index):
env = env_creator(config["env_config"])
env = wrap_dqn(registry, env, config["model"])
env = wrap_dqn(registry, env, config["model"], config["random_starts"])
self.env = env
self.config = config

16
python/ray/rllib/optimizers/apex_optimizer.py
View file

@ -28,7 +28,7 @@ class ReplayActor(object):
def __init__(
self, num_shards, learning_starts, buffer_size, train_batch_size,
prioritized_replay_alpha, prioritized_replay_beta,
prioritized_replay_eps):
prioritized_replay_eps, clip_rewards):
self.replay_starts = learning_starts // num_shards
self.buffer_size = buffer_size // num_shards
self.train_batch_size = train_batch_size
@ -36,7 +36,8 @@ class ReplayActor(object):
self.prioritized_replay_eps = prioritized_replay_eps
self.replay_buffer = PrioritizedReplayBuffer(
buffer_size, alpha=prioritized_replay_alpha)
self.buffer_size, alpha=prioritized_replay_alpha,
clip_rewards=clip_rewards)
# Metrics
self.add_batch_timer = TimerStat()
@ -98,6 +99,7 @@ class GenericLearner(threading.Thread):
self.queue_timer = TimerStat()
self.grad_timer = TimerStat()
self.daemon = True
self.weights_updated = False
def run(self):
while True:
@ -111,6 +113,7 @@ class GenericLearner(threading.Thread):
td_error = self.local_evaluator.compute_apply(replay)
self.outqueue.put((ra, replay, td_error))
self.learner_queue_size.push(self.inqueue.qsize())
self.weights_updated = True
class ApexOptimizer(Optimizer):
@ -121,7 +124,7 @@ class ApexOptimizer(Optimizer):
prioritized_replay_beta=0.4, prioritized_replay_eps=1e-6,
train_batch_size=512, sample_batch_size=50,
num_replay_buffer_shards=1, max_weight_sync_delay=400,
debug=False):
clip_rewards=True, debug=False):
self.debug = debug
self.replay_starts = learning_starts
@ -138,7 +141,7 @@ class ApexOptimizer(Optimizer):
ReplayActor,
[num_replay_buffer_shards, learning_starts, buffer_size,
train_batch_size, prioritized_replay_alpha,
prioritized_replay_beta, prioritized_replay_eps],
prioritized_replay_beta, prioritized_replay_eps, clip_rewards],
num_replay_buffer_shards)
assert len(self.remote_evaluators) > 0
@ -199,7 +202,10 @@ class ApexOptimizer(Optimizer):
# Update weights if needed
self.steps_since_update[ev] += self.sample_batch_size
if self.steps_since_update[ev] >= self.max_weight_sync_delay:
if weights is None:
# Note that it's important to pull new weights once
# updated to avoid excessive correlation between actors
if weights is None or self.learner.weights_updated:
self.learner.weights_updated = False
with self.timers["put_weights"]:
weights = ray.put(
self.local_evaluator.get_weights())

8
python/ray/rllib/optimizers/local_sync_replay.py
View file

@ -20,7 +20,7 @@ class LocalSyncReplayOptimizer(Optimizer):
self, learning_starts=1000, buffer_size=10000,
prioritized_replay=True, prioritized_replay_alpha=0.6,
prioritized_replay_beta=0.4, prioritized_replay_eps=1e-6,
train_batch_size=32, sample_batch_size=4):
train_batch_size=32, sample_batch_size=4, clip_rewards=True):
self.replay_starts = learning_starts
self.prioritized_replay_beta = prioritized_replay_beta
@ -37,10 +37,10 @@ class LocalSyncReplayOptimizer(Optimizer):
# Set up replay buffer
if prioritized_replay:
self.replay_buffer = PrioritizedReplayBuffer(
buffer_size,
alpha=prioritized_replay_alpha)
buffer_size, alpha=prioritized_replay_alpha,
clip_rewards=clip_rewards)
else:
self.replay_buffer = ReplayBuffer(buffer_size)
self.replay_buffer = ReplayBuffer(buffer_size, clip_rewards)
assert buffer_size >= self.replay_starts

13
python/ray/rllib/optimizers/replay_buffer.py
View file

@ -12,7 +12,7 @@ from ray.rllib.utils.window_stat import WindowStat
class ReplayBuffer(object):
def __init__(self, size):
def __init__(self, size, clip_rewards):
"""Create Prioritized Replay buffer.
Parameters
@ -30,11 +30,15 @@ class ReplayBuffer(object):
self._num_sampled = 0
self._evicted_hit_stats = WindowStat("evicted_hit", 1000)
self._est_size_bytes = 0
self._clip_rewards = clip_rewards
def __len__(self):
return len(self._storage)
def add(self, obs_t, action, reward, obs_tp1, done, weight):
if self._clip_rewards:
reward = np.sign(reward)
data = (obs_t, action, reward, obs_tp1, done)
self._num_added += 1
@ -103,7 +107,7 @@ class ReplayBuffer(object):
class PrioritizedReplayBuffer(ReplayBuffer):
def __init__(self, size, alpha):
def __init__(self, size, alpha, clip_rewards):
"""Create Prioritized Replay buffer.
Parameters
@ -119,7 +123,7 @@ class PrioritizedReplayBuffer(ReplayBuffer):
--------
ReplayBuffer.__init__
"""
super(PrioritizedReplayBuffer, self).__init__(size)
super(PrioritizedReplayBuffer, self).__init__(size, clip_rewards)
assert alpha > 0
self._alpha = alpha
@ -134,6 +138,9 @@ class PrioritizedReplayBuffer(ReplayBuffer):
def add(self, obs_t, action, reward, obs_tp1, done, weight):
"""See ReplayBuffer.store_effect"""
if self._clip_rewards:
reward = np.sign(reward)
idx = self._next_idx
super(PrioritizedReplayBuffer, self).add(
obs_t, action, reward, obs_tp1, done, weight)

7
python/ray/rllib/tuned_examples/pong-apex.yaml
View file

@ -1,5 +1,7 @@
# This can be expected to reach 20.8 reward within an hour when using
# a V100 GPU (e.g. p3.2xl instance on AWS, and m4.4xl workers).
pong-apex:
env: Pong-v0
env: PongNoFrameskip-v4
run: APEX
resources:
cpu:
@ -7,8 +9,7 @@ pong-apex:
gpu: 1
config:
force_evaluators_remote: True # requires cluster
target_network_update_freq: 50000
num_workers: 32
lr: .0001
gamma: 0.99
model:
grayscale: True

2
python/ray/tune/logger.py
View file

@ -187,4 +187,4 @@ def pretty_print(result):
out[k] = v
cleaned = json.dumps(out, cls=_CustomEncoder)
return yaml.dump(json.loads(cleaned), default_flow_style=False)
return yaml.safe_dump(json.loads(cleaned), default_flow_style=False)