add a temp copy of TF agents (until the API stops changing or configs.py are included)

examples/pybullet/gym/pybullet_envs/agents/tools/mock_environment.py

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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.
+
+"""Mock environment for testing reinforcement learning code."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import gym
+import gym.spaces
+import numpy as np
+
+
+class MockEnvironment(object):
+  """Generate random agent input and keep track of statistics."""
+
+  def __init__(self, observ_shape, action_shape, min_duration, max_duration):
+    """Generate random agent input and keep track of statistics.
+
+    Args:
+      observ_shape: Shape for the random observations.
+      action_shape: Shape for the action space.
+      min_duration: Minimum number of steps per episode.
+      max_duration: Maximum number of steps per episode.
+
+    Attributes:
+      steps: List of actual simulated lengths for all episodes.
+      durations: List of decided lengths for all episodes.
+    """
+    self._observ_shape = observ_shape
+    self._action_shape = action_shape
+    self._min_duration = min_duration
+    self._max_duration = max_duration
+    self._random = np.random.RandomState(0)
+    self.steps = []
+    self.durations = []
+
+  @property
+  def observation_space(self):
+    low = np.zeros(self._observ_shape)
+    high = np.ones(self._observ_shape)
+    return gym.spaces.Box(low, high)
+
+  @property
+  def action_space(self):
+    low = np.zeros(self._action_shape)
+    high = np.ones(self._action_shape)
+    return gym.spaces.Box(low, high)
+
+  @property
+  def unwrapped(self):
+    return self
+
+  def step(self, action):
+    assert self.action_space.contains(action)
+    assert self.steps[-1] < self.durations[-1]
+    self.steps[-1] += 1
+    observ = self._current_observation()
+    reward = self._current_reward()
+    done = self.steps[-1] >= self.durations[-1]
+    info = {}
+    return observ, reward, done, info
+
+  def reset(self):
+    duration = self._random.randint(self._min_duration, self._max_duration + 1)
+    self.steps.append(0)
+    self.durations.append(duration)
+    return self._current_observation()
+
+  def _current_observation(self):
+    return self._random.uniform(0, 1, self._observ_shape)
+
+  def _current_reward(self):
+    return self._random.uniform(-1, 1)