pre-release 0.10.0

rl_coach/exploration_policies/truncated_normal.py

@@ -0,0 +1,100 @@
+#
+# 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.
+#
+
+from typing import List
+
+import numpy as np
+from rl_coach.schedules import Schedule, LinearSchedule
+from scipy.stats import truncnorm
+from rl_coach.spaces import ActionSpace, BoxActionSpace
+
+from rl_coach.core_types import RunPhase, ActionType
+from rl_coach.exploration_policies.exploration_policy import ExplorationPolicy, ExplorationParameters
+
+
+class TruncatedNormalParameters(ExplorationParameters):
+    def __init__(self):
+        super().__init__()
+        self.noise_percentage_schedule = LinearSchedule(0.1, 0.1, 50000)
+        self.evaluation_noise_percentage = 0.05
+        self.clip_low = 0
+        self.clip_high = 1
+
+    @property
+    def path(self):
+        return 'rl_coach.exploration_policies.truncated_normal:TruncatedNormal'
+
+
+class TruncatedNormal(ExplorationPolicy):
+    def __init__(self, action_space: ActionSpace, noise_percentage_schedule: Schedule,
+                 evaluation_noise_percentage: float, clip_low: float, clip_high: float):
+        """
+        :param action_space: the action space used by the environment
+        :param noise_percentage_schedule: the schedule for the noise variance percentage relative to the absolute range
+                                          of the action space
+        :param evaluation_noise_percentage: the noise variance percentage that will be used during evaluation phases
+        """
+        super().__init__(action_space)
+        self.noise_percentage_schedule = noise_percentage_schedule
+        self.evaluation_noise_percentage = evaluation_noise_percentage
+        self.clip_low = clip_low
+        self.clip_high = clip_high
+
+        if not isinstance(action_space, BoxActionSpace):
+            raise ValueError("Truncated normal exploration works only for continuous controls."
+                             "The given action space is of type: {}".format(action_space.__class__.__name__))
+
+        if not np.all(-np.inf < action_space.high) or not np.all(action_space.high < np.inf)\
+                or not np.all(-np.inf < action_space.low) or not np.all(action_space.low < np.inf):
+            raise ValueError("Additive noise exploration requires bounded actions")
+
+        # TODO: allow working with unbounded actions by defining the noise in terms of range and not percentage
+
+    def get_action(self, action_values: List[ActionType]) -> ActionType:
+        # set the current noise percentage
+        if self.phase == RunPhase.TEST:
+            current_noise_precentage = self.evaluation_noise_percentage
+        else:
+            current_noise_precentage = self.noise_percentage_schedule.current_value
+
+        # scale the noise to the action space range
+        action_values_std = current_noise_precentage * (self.action_space.high - self.action_space.low)
+
+        # extract the mean values
+        if isinstance(action_values, list):
+            # the action values are expected to be a list with the action mean and optionally the action stdev
+            action_values_mean = action_values[0].squeeze()
+        else:
+            # the action values are expected to be a numpy array representing the action mean
+            action_values_mean = action_values.squeeze()
+
+        # step the noise schedule
+        if self.phase == RunPhase.TRAIN:
+            self.noise_percentage_schedule.step()
+            # the second element of the list is assumed to be the standard deviation
+            if isinstance(action_values, list) and len(action_values) > 1:
+                action_values_std = action_values[1].squeeze()
+
+        # sample from truncated normal distribution
+        normalized_low = (self.clip_low - action_values_mean) / action_values_std
+        normalized_high = (self.clip_high - action_values_mean) / action_values_std
+        distribution = truncnorm(normalized_low, normalized_high, loc=action_values_mean, scale=action_values_std)
+        action = distribution.rvs(1)
+
+        return action
+
+    def get_control_param(self):
+        return np.ones(self.action_space.shape)*self.noise_percentage_schedule.current_value