pre-release 0.10.0

rl_coach/agents/agent_interface.py

@@ -0,0 +1,125 @@
+#
+# 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 Union, List, Dict
+
+import numpy as np
+
+from rl_coach.core_types import EnvResponse, ActionInfo, RunPhase, PredictionType, ActionType
+
+
+class AgentInterface(object):
+    def __init__(self):
+        self._phase = RunPhase.HEATUP
+        self._parent = None
+        self.spaces = None
+
+    @property
+    def parent(self):
+        """
+        Get the parent class of the agent
+        :return: the current phase
+        """
+        return self._parent
+
+    @parent.setter
+    def parent(self, val):
+        """
+        Change the parent class of the agent
+        :param val: the new parent
+        :return: None
+        """
+        self._parent = val
+
+    @property
+    def phase(self) -> RunPhase:
+        """
+        Get the phase of the agent
+        :return: the current phase
+        """
+        return self._phase
+
+    @phase.setter
+    def phase(self, val: RunPhase):
+        """
+        Change the phase of the agent
+        :param val: the new phase
+        :return: None
+        """
+        self._phase = val
+
+    def reset_internal_state(self) -> None:
+        """
+        Reset the episode parameters for the agent
+        :return: None
+        """
+        raise NotImplementedError("")
+
+    def train(self) -> Union[float, List]:
+        """
+        Train the agents network
+        :return: The loss of the training
+        """
+        raise NotImplementedError("")
+
+    def act(self) -> ActionInfo:
+        """
+        Get a decision of the next action to take.
+        The action is dependent on the current state which the agent holds from resetting the environment or from
+        the observe function.
+        :return: A tuple containing the actual action and additional info on the action
+        """
+        raise NotImplementedError("")
+
+    def observe(self, env_response: EnvResponse) -> bool:
+        """
+        Gets a response from the environment.
+        Processes this information for later use. For example, create a transition and store it in memory.
+        The action info (a class containing any info the agent wants to store regarding its action decision process) is
+        stored by the agent itself when deciding on the action.
+        :param env_response: a EnvResponse containing the response from the environment
+        :return: a done signal which is based on the agent knowledge. This can be different from the done signal from
+                 the environment. For example, an agent can decide to finish the episode each time it gets some
+                 intrinsic reward
+        """
+        raise NotImplementedError("")
+
+    def save_checkpoint(self, checkpoint_id: int) -> None:
+        """
+        Save the model of the agent to the disk. This can contain the network parameters, the memory of the agent, etc.
+        :param checkpoint_id: the checkpoint id to use for saving
+        :return: None
+        """
+        raise NotImplementedError("")
+
+    def get_predictions(self, states: Dict, prediction_type: PredictionType) -> np.ndarray:
+        """
+        Get a prediction from the agent with regard to the requested prediction_type. If the agent cannot predict this
+        type of prediction_type, or if there is more than possible way to do so, raise a ValueException.
+        :param states:
+        :param prediction_type:
+        :return: the agent's prediction
+        """
+        raise NotImplementedError("")
+
+    def set_incoming_directive(self, action: ActionType) -> None:
+        """
+        Pass a higher level command (directive) to the agent.
+        For example, a higher level agent can set the goal of the agent.
+        :param action: the directive to pass to the agent
+        :return: None
+        """
+        raise NotImplementedError("")