pre-release 0.10.0

rl_coach/agents/naf_agent.py

@@ -0,0 +1,126 @@
+#
+# 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.value_optimization_agent import ValueOptimizationAgent
+from rl_coach.architectures.tensorflow_components.heads.naf_head import NAFHeadParameters
+from rl_coach.architectures.tensorflow_components.middlewares.fc_middleware import FCMiddlewareParameters
+from rl_coach.base_parameters import AlgorithmParameters, AgentParameters, \
+    NetworkParameters, InputEmbedderParameters
+from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters
+from rl_coach.spaces import BoxActionSpace
+
+from rl_coach.core_types import ActionInfo, EnvironmentSteps
+from rl_coach.exploration_policies.ou_process import OUProcessParameters
+
+
+class NAFNetworkParameters(NetworkParameters):
+    def __init__(self):
+        super().__init__()
+        self.input_embedders_parameters = {'observation': InputEmbedderParameters()}
+        self.middleware_parameters = FCMiddlewareParameters()
+        self.heads_parameters = [NAFHeadParameters()]
+        self.loss_weights = [1.0]
+        self.optimizer_type = 'Adam'
+        self.learning_rate = 0.001
+        self.async_training = True
+        self.create_target_network = True
+
+
+class NAFAlgorithmParameters(AlgorithmParameters):
+    def __init__(self):
+        super().__init__()
+        self.num_consecutive_training_steps = 5
+        self.num_steps_between_copying_online_weights_to_target = EnvironmentSteps(1)
+        self.rate_for_copying_weights_to_target = 0.001
+
+
+class NAFAgentParameters(AgentParameters):
+    def __init__(self):
+        super().__init__(algorithm=NAFAlgorithmParameters(),
+                         exploration=OUProcessParameters(),
+                         memory=EpisodicExperienceReplayParameters(),
+                         networks={"main": NAFNetworkParameters()})
+
+    @property
+    def path(self):
+        return 'rl_coach.agents.naf_agent:NAFAgent'
+
+
+# Normalized Advantage Functions - https://arxiv.org/pdf/1603.00748.pdf
+class NAFAgent(ValueOptimizationAgent):
+    def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
+        super().__init__(agent_parameters, parent)
+        self.l_values = self.register_signal("L")
+        self.a_values = self.register_signal("Advantage")
+        self.mu_values = self.register_signal("Action")
+        self.v_values = self.register_signal("V")
+        self.TD_targets = self.register_signal("TD targets")
+
+    def learn_from_batch(self, batch):
+        network_keys = self.ap.network_wrappers['main'].input_embedders_parameters.keys()
+
+        # TD error = r + discount*v_st_plus_1 - q_st
+        v_st_plus_1 = self.networks['main'].target_network.predict(
+            batch.next_states(network_keys),
+            self.networks['main'].target_network.output_heads[0].V,
+            squeeze_output=False,
+        )
+        TD_targets = np.expand_dims(batch.rewards(), -1) + \
+                     (1.0 - np.expand_dims(batch.game_overs(), -1)) * self.ap.algorithm.discount * v_st_plus_1
+
+        self.TD_targets.add_sample(TD_targets)
+
+        result = self.networks['main'].train_and_sync_networks({**batch.states(network_keys),
+                                                                'output_0_0': batch.actions(len(batch.actions().shape) == 1)
+                                                                }, TD_targets)
+        total_loss, losses, unclipped_grads = result[:3]
+
+        return total_loss, losses, unclipped_grads
+
+    def choose_action(self, curr_state):
+        if type(self.spaces.action) != BoxActionSpace:
+            raise ValueError('NAF works only for continuous control problems')
+
+        # convert to batch so we can run it through the network
+        tf_input_state = self.prepare_batch_for_inference(curr_state, 'main')
+        naf_head = self.networks['main'].online_network.output_heads[0]
+        action_values = self.networks['main'].online_network.predict(tf_input_state, outputs=naf_head.mu,
+                                                                     squeeze_output=False)
+
+        # get the actual action to use
+        action = self.exploration_policy.get_action(action_values)
+
+        # get the internal values for logging
+        outputs = [naf_head.mu, naf_head.Q, naf_head.L, naf_head.A, naf_head.V]
+        result = self.networks['main'].online_network.predict(
+            {**tf_input_state, 'output_0_0': action_values},
+            outputs=outputs
+        )
+        mu, Q, L, A, V = result
+
+        # store the q values statistics for logging
+        self.q_values.add_sample(Q)
+        self.l_values.add_sample(L)
+        self.a_values.add_sample(A)
+        self.mu_values.add_sample(mu)
+        self.v_values.add_sample(V)
+
+        action_info = ActionInfo(action=action, action_value=Q)
+        
+        return action_info