pre-release 0.10.0

rl_coach/agents/bc_agent.py

@@ -0,0 +1,81 @@
+#
+# 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
+
+import numpy as np
+from rl_coach.agents.imitation_agent import ImitationAgent
+from rl_coach.architectures.tensorflow_components.heads.policy_head import PolicyHeadParameters
+from rl_coach.architectures.tensorflow_components.middlewares.fc_middleware import FCMiddlewareParameters
+from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters
+
+from rl_coach.base_parameters import AgentParameters, AlgorithmParameters, NetworkParameters, InputEmbedderParameters, \
+    MiddlewareScheme
+from rl_coach.exploration_policies.e_greedy import EGreedyParameters
+
+
+class BCAlgorithmParameters(AlgorithmParameters):
+    def __init__(self):
+        super().__init__()
+        self.collect_new_data = False
+
+
+class BCNetworkParameters(NetworkParameters):
+    def __init__(self):
+        super().__init__()
+        self.input_embedders_parameters = {'observation': InputEmbedderParameters()}
+        self.middleware_parameters = FCMiddlewareParameters(scheme=MiddlewareScheme.Medium)
+        self.heads_parameters = [PolicyHeadParameters()]
+        self.loss_weights = [1.0]
+        self.optimizer_type = 'Adam'
+        self.batch_size = 32
+        self.replace_mse_with_huber_loss = False
+        self.create_target_network = False
+
+
+class BCAgentParameters(AgentParameters):
+    def __init__(self):
+        super().__init__(algorithm=BCAlgorithmParameters(),
+                         exploration=EGreedyParameters(),
+                         memory=EpisodicExperienceReplayParameters(),
+                         networks={"main": BCNetworkParameters()})
+
+    @property
+    def path(self):
+        return 'rl_coach.agents.bc_agent:BCAgent'
+
+
+# Behavioral Cloning Agent
+class BCAgent(ImitationAgent):
+    def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
+        super().__init__(agent_parameters, parent)
+
+    def learn_from_batch(self, batch):
+        network_keys = self.ap.network_wrappers['main'].input_embedders_parameters.keys()
+
+        # When using a policy head, the targets refer to the advantages that we are normally feeding the head with.
+        # In this case, we need the policy head to just predict probabilities, so while we usually train the network
+        # with log(Pi)*Advantages, in this specific case we will train it to log(Pi), which after the softmax will
+        # predict Pi (=probabilities)
+        targets = np.ones(batch.actions().shape[0])
+
+        result = self.networks['main'].train_and_sync_networks({**batch.states(network_keys),
+                                                                'output_0_0': batch.actions()},
+                                                               targets)
+        total_loss, losses, unclipped_grads = result[:3]
+
+        return total_loss, losses, unclipped_grads
+