pre-release 0.10.0

rl_coach/exploration_policies/bootstrapped.py

@@ -0,0 +1,77 @@
+#
+# 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.exploration_policies.additive_noise import AdditiveNoiseParameters
+from rl_coach.exploration_policies.e_greedy import EGreedy, EGreedyParameters
+from rl_coach.schedules import Schedule, LinearSchedule
+from rl_coach.spaces import ActionSpace
+
+from rl_coach.core_types import RunPhase, ActionType
+from rl_coach.exploration_policies.exploration_policy import ExplorationParameters
+
+
+class BootstrappedParameters(EGreedyParameters):
+    def __init__(self):
+        super().__init__()
+        self.architecture_num_q_heads = 10
+        self.bootstrapped_data_sharing_probability = 1.0
+        self.epsilon_schedule = LinearSchedule(1, 0.01, 1000000)
+
+    @property
+    def path(self):
+        return 'rl_coach.exploration_policies.bootstrapped:Bootstrapped'
+
+
+class Bootstrapped(EGreedy):
+    def __init__(self, action_space: ActionSpace, epsilon_schedule: Schedule, evaluation_epsilon: float,
+                 architecture_num_q_heads: int,
+                 continuous_exploration_policy_parameters: ExplorationParameters = AdditiveNoiseParameters(),):
+        """
+        :param action_space: the action space used by the environment
+        :param epsilon_schedule: a schedule for the epsilon values
+        :param evaluation_epsilon: the epsilon value to use for evaluation phases
+        :param continuous_exploration_policy_parameters: the parameters of the continuous exploration policy to use
+                                                         if the e-greedy is used for a continuous policy
+        :param architecture_num_q_heads: the number of q heads to select from
+        """
+        super().__init__(action_space, epsilon_schedule, evaluation_epsilon, continuous_exploration_policy_parameters)
+        self.num_heads = architecture_num_q_heads
+        self.selected_head = 0
+        self.last_action_values = 0
+
+    def select_head(self):
+        self.selected_head = np.random.randint(self.num_heads)
+
+    def get_action(self, action_values: List[ActionType]) -> ActionType:
+        # action values are none in case the exploration policy is going to select a random action
+        if action_values is not None:
+            if self.phase == RunPhase.TRAIN:
+                action_values = action_values[self.selected_head]
+            else:
+                # ensemble voting for evaluation
+                top_action_votings = np.argmax(action_values, axis=-1)
+                counts = np.bincount(top_action_votings.squeeze())
+                top_action = np.argmax(counts)
+                # convert the top action to a one hot vector and pass it to e-greedy
+                action_values = np.eye(len(self.action_space.actions))[[top_action]]
+        self.last_action_values = action_values
+        return super().get_action(action_values)
+
+    def get_control_param(self):
+        return self.selected_head