initial CIL implementation (WIP)

rl_coach/agents/cil_agent.py

@@ -0,0 +1,84 @@
+#
+# 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
+
+from rl_coach.agents.imitation_agent import ImitationAgent
+from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters
+from rl_coach.architectures.tensorflow_components.heads.cil_head import RegressionHeadParameters
+from rl_coach.architectures.tensorflow_components.middlewares.fc_middleware import FCMiddlewareParameters
+from rl_coach.base_parameters import AgentParameters, MiddlewareScheme, NetworkParameters, AlgorithmParameters
+from rl_coach.exploration_policies.e_greedy import EGreedyParameters
+from rl_coach.memories.non_episodic.balanced_experience_replay import BalancedExperienceReplayParameters
+
+
+class CILAlgorithmParameters(AlgorithmParameters):
+    def __init__(self):
+        super().__init__()
+        self.collect_new_data = False
+
+
+class CILNetworkParameters(NetworkParameters):
+    def __init__(self):
+        super().__init__()
+        self.input_embedders_parameters = {'observation': InputEmbedderParameters()}
+        self.middleware_parameters = FCMiddlewareParameters(scheme=MiddlewareScheme.Medium)
+        self.heads_parameters = [RegressionHeadParameters()]
+        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 CILAgentParameters(AgentParameters):
+    def __init__(self):
+        super().__init__(algorithm=CILAlgorithmParameters(),
+                         exploration=EGreedyParameters(),
+                         memory=BalancedExperienceReplayParameters(),
+                         networks={"main": CILNetworkParameters()})
+
+    @property
+    def path(self):
+        return 'rl_coach.agents.cil_agent:CILAgent'
+
+
+# Conditional Imitation Learning Agent: https://arxiv.org/abs/1710.02410
+class CILAgent(ImitationAgent):
+    def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
+        super().__init__(agent_parameters, parent)
+        self.current_high_level_control = 0
+
+    def choose_action(self, curr_state):
+        self.current_high_level_control = curr_state['high_level_command']
+        return super().choose_action(curr_state)
+
+    def extract_action_values(self, prediction):
+        return prediction[self.current_high_level_control].squeeze()
+
+    def learn_from_batch(self, batch):
+        network_keys = self.ap.network_wrappers['main'].input_embedders_parameters.keys()
+
+        target_values = self.networks['main'].online_network.predict({**batch.states(network_keys)})
+
+        branch_to_update = batch.states(['high_level_command'])['high_level_command']
+        for idx, branch in enumerate(branch_to_update):
+            target_values[branch][idx] = batch.actions()[idx]
+
+        result = self.networks['main'].train_and_sync_networks({**batch.states(network_keys)}, target_values)
+        total_loss, losses, unclipped_grads = result[:3]
+
+        return total_loss, losses, unclipped_grads