pre-release 0.10.0

rl_coach/environments/control_suite_environment.py

@@ -0,0 +1,162 @@
+#
+# Copyright (c) 2017 Intel Corporation
+#
+# 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.
+#
+
+
+
+import random
+from enum import Enum
+from typing import Union
+
+import numpy as np
+
+try:
+    from dm_control import suite
+    from dm_control.suite.wrappers import pixels
+except ImportError:
+    from rl_coach.logger import failed_imports
+    failed_imports.append("DeepMind Control Suite")
+
+from rl_coach.base_parameters import VisualizationParameters
+from rl_coach.environments.environment import Environment, EnvironmentParameters, LevelSelection
+from rl_coach.filters.filter import NoInputFilter, NoOutputFilter
+from rl_coach.spaces import BoxActionSpace, ImageObservationSpace, VectorObservationSpace, StateSpace
+
+
+class ObservationType(Enum):
+    Measurements = 1
+    Image = 2
+    Image_and_Measurements = 3
+
+
+# Parameters
+class ControlSuiteEnvironmentParameters(EnvironmentParameters):
+    def __init__(self):
+        super().__init__()
+        self.observation_type = ObservationType.Measurements
+        self.default_input_filter = ControlSuiteInputFilter
+        self.default_output_filter = ControlSuiteOutputFilter
+
+    @property
+    def path(self):
+        return 'rl_coach.environments.control_suite_environment:ControlSuiteEnvironment'
+
+
+"""
+ControlSuite Environment Components
+"""
+ControlSuiteInputFilter = NoInputFilter()
+ControlSuiteOutputFilter = NoOutputFilter()
+
+control_suite_envs = {':'.join(env): ':'.join(env) for env in suite.BENCHMARKING}
+
+
+# Environment
+class ControlSuiteEnvironment(Environment):
+    def __init__(self, level: LevelSelection, frame_skip: int, visualization_parameters: VisualizationParameters,
+                 seed: Union[None, int]=None, human_control: bool=False,
+                 observation_type: ObservationType=ObservationType.Measurements,
+                 custom_reward_threshold: Union[int, float]=None, **kwargs):
+        super().__init__(level, seed, frame_skip, human_control, custom_reward_threshold, visualization_parameters)
+
+        self.observation_type = observation_type
+
+        # load and initialize environment
+        domain_name, task_name = self.env_id.split(":")
+        self.env = suite.load(domain_name=domain_name, task_name=task_name)
+
+        if observation_type != ObservationType.Measurements:
+            self.env = pixels.Wrapper(self.env, pixels_only=observation_type == ObservationType.Image)
+
+        # seed
+        if self.seed is not None:
+            np.random.seed(self.seed)
+            random.seed(self.seed)
+
+        self.state_space = StateSpace({})
+
+        # image observations
+        if observation_type != ObservationType.Measurements:
+            self.state_space['pixels'] = ImageObservationSpace(shape=self.env.observation_spec()['pixels'].shape,
+                                                               high=255)
+
+        # measurements observations
+        if observation_type != ObservationType.Image:
+            measurements_space_size = 0
+            measurements_names = []
+            for observation_space_name, observation_space in self.env.observation_spec().items():
+                if len(observation_space.shape) == 0:
+                    measurements_space_size += 1
+                    measurements_names.append(observation_space_name)
+                elif len(observation_space.shape) == 1:
+                    measurements_space_size += observation_space.shape[0]
+                    measurements_names.extend(["{}_{}".format(observation_space_name, i) for i in
+                                               range(observation_space.shape[0])])
+            self.state_space['measurements'] = VectorObservationSpace(shape=measurements_space_size,
+                                                                      measurements_names=measurements_names)
+
+        # actions
+        self.action_space = BoxActionSpace(
+            shape=self.env.action_spec().shape[0],
+            low=self.env.action_spec().minimum,
+            high=self.env.action_spec().maximum
+        )
+
+        # initialize the state by getting a new state from the environment
+        self.reset_internal_state(True)
+
+        # render
+        if self.is_rendered:
+            image = self.get_rendered_image()
+            scale = 1
+            if self.human_control:
+                scale = 2
+            if not self.native_rendering:
+                self.renderer.create_screen(image.shape[1]*scale, image.shape[0]*scale)
+
+    def _update_state(self):
+        self.state = {}
+
+        if self.observation_type != ObservationType.Measurements:
+            self.pixels = self.last_result.observation['pixels']
+            self.state['pixels'] = self.pixels
+
+        if self.observation_type != ObservationType.Image:
+            self.measurements = np.array([])
+            for sub_observation in self.last_result.observation.values():
+                if isinstance(sub_observation, np.ndarray) and len(sub_observation.shape) == 1:
+                    self.measurements = np.concatenate((self.measurements, sub_observation))
+                else:
+                    self.measurements = np.concatenate((self.measurements, np.array([sub_observation])))
+            self.state['measurements'] = self.measurements
+
+        self.reward = self.last_result.reward if self.last_result.reward is not None else 0
+
+        self.done = self.last_result.last()
+
+    def _take_action(self, action):
+        if type(self.action_space) == BoxActionSpace:
+            action = self.action_space.clip_action_to_space(action)
+
+        self.last_result = self.env.step(action)
+
+    def _restart_environment_episode(self, force_environment_reset=False):
+        self.last_result = self.env.reset()
+
+    def _render(self):
+        pass
+
+    def get_rendered_image(self):
+        return self.env.physics.render(camera_id=0)