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# Copyright 2017 The TensorFlow Agents Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Batch of environments inside the TensorFlow graph."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import gym
import tensorflow as tf
class InGraphBatchEnv(object):
"""Batch of environments inside the TensorFlow graph.
The batch of environments will be stepped and reset inside of the graph using
a tf.py_func(). The current batch of observations, actions, rewards, and done
flags are held in according variables.
"""
def __init__(self, batch_env):
"""Batch of environments inside the TensorFlow graph.
Args:
batch_env: Batch environment.
"""
self._batch_env = batch_env
observ_shape = self._parse_shape(self._batch_env.observation_space)
observ_dtype = self._parse_dtype(self._batch_env.observation_space)
action_shape = self._parse_shape(self._batch_env.action_space)
action_dtype = self._parse_dtype(self._batch_env.action_space)
with tf.variable_scope('env_temporary'):
self._observ = tf.Variable(tf.zeros((len(self._batch_env),) + observ_shape, observ_dtype),
name='observ',
trainable=False)
self._action = tf.Variable(tf.zeros((len(self._batch_env),) + action_shape, action_dtype),
name='action',
trainable=False)
self._reward = tf.Variable(tf.zeros((len(self._batch_env),), tf.float32),
name='reward',
trainable=False)
self._done = tf.Variable(tf.cast(tf.ones((len(self._batch_env),)), tf.bool),
name='done',
trainable=False)
def __getattr__(self, name):
"""Forward unimplemented attributes to one of the original environments.
Args:
name: Attribute that was accessed.
Returns:
Value behind the attribute name in one of the original environments.
"""
return getattr(self._batch_env, name)
def __len__(self):
"""Number of combined environments."""
return len(self._batch_env)
def __getitem__(self, index):
"""Access an underlying environment by index."""
return self._batch_env[index]
def simulate(self, action):
"""Step the batch of environments.
The results of the step can be accessed from the variables defined below.
Args:
action: Tensor holding the batch of actions to apply.
Returns:
Operation.
"""
with tf.name_scope('environment/simulate'):
if action.dtype in (tf.float16, tf.float32, tf.float64):
action = tf.check_numerics(action, 'action')
observ_dtype = self._parse_dtype(self._batch_env.observation_space)
observ, reward, done = tf.py_func(lambda a: self._batch_env.step(a)[:3], [action],
[observ_dtype, tf.float32, tf.bool],
name='step')
observ = tf.check_numerics(observ, 'observ')
reward = tf.check_numerics(reward, 'reward')
return tf.group(self._observ.assign(observ), self._action.assign(action),
self._reward.assign(reward), self._done.assign(done))
def reset(self, indices=None):
"""Reset the batch of environments.
Args:
indices: The batch indices of the environments to reset; defaults to all.
Returns:
Batch tensor of the new observations.
"""
if indices is None:
indices = tf.range(len(self._batch_env))
observ_dtype = self._parse_dtype(self._batch_env.observation_space)
observ = tf.py_func(self._batch_env.reset, [indices], observ_dtype, name='reset')
observ = tf.check_numerics(observ, 'observ')
reward = tf.zeros_like(indices, tf.float32)
done = tf.zeros_like(indices, tf.bool)
with tf.control_dependencies([
tf.scatter_update(self._observ, indices, observ),
tf.scatter_update(self._reward, indices, reward),
tf.scatter_update(self._done, indices, done)
]):
return tf.identity(observ)
@property
def observ(self):
"""Access the variable holding the current observation."""
return self._observ
@property
def action(self):
"""Access the variable holding the last recieved action."""
return self._action
@property
def reward(self):
"""Access the variable holding the current reward."""
return self._reward
@property
def done(self):
"""Access the variable indicating whether the episode is done."""
return self._done
def close(self):
"""Send close messages to the external process and join them."""
self._batch_env.close()
def _parse_shape(self, space):
"""Get a tensor shape from a OpenAI Gym space.
Args:
space: Gym space.
Returns:
Shape tuple.
"""
if isinstance(space, gym.spaces.Discrete):
return ()
if isinstance(space, gym.spaces.Box):
return space.shape
raise NotImplementedError()
def _parse_dtype(self, space):
"""Get a tensor dtype from a OpenAI Gym space.
Args:
space: Gym space.
Returns:
TensorFlow data type.
"""
if isinstance(space, gym.spaces.Discrete):
return tf.int32
if isinstance(space, gym.spaces.Box):
return tf.float32
raise NotImplementedError()
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