pre-release 0.10.0

rl_coach/agents/nec_agent.py

@@ -0,0 +1,176 @@
+#
+# 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.
+#
+
+import os
+import pickle
+from typing import Union
+
+import numpy as np
+from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent
+from rl_coach.architectures.tensorflow_components.heads.dnd_q_head import DNDQHeadParameters
+from rl_coach.architectures.tensorflow_components.middlewares.fc_middleware import FCMiddlewareParameters
+from rl_coach.base_parameters import AlgorithmParameters, NetworkParameters, AgentParameters, \
+    InputEmbedderParameters
+from rl_coach.core_types import RunPhase, EnvironmentSteps, Episode, StateType
+from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters, MemoryGranularity
+from rl_coach.schedules import ConstantSchedule
+
+from rl_coach.exploration_policies.e_greedy import EGreedyParameters
+from rl_coach.logger import screen
+
+
+class NECNetworkParameters(NetworkParameters):
+    def __init__(self):
+        super().__init__()
+        self.input_embedders_parameters = {'observation': InputEmbedderParameters()}
+        self.middleware_parameters = FCMiddlewareParameters()
+        self.heads_parameters = [DNDQHeadParameters()]
+        self.loss_weights = [1.0]
+        self.rescale_gradient_from_head_by_factor = [1]
+        self.optimizer_type = 'Adam'
+
+
+class NECAlgorithmParameters(AlgorithmParameters):
+    def __init__(self):
+        super().__init__()
+        self.dnd_size = 500000
+        self.l2_norm_added_delta = 0.001
+        self.new_value_shift_coefficient = 0.1
+        self.number_of_knn = 50
+        self.DND_key_error_threshold = 0
+        self.num_consecutive_playing_steps = EnvironmentSteps(4)
+        self.propagate_updates_to_DND = False
+        self.n_step = 100
+        self.bootstrap_total_return_from_old_policy = True
+
+
+class NECMemoryParameters(EpisodicExperienceReplayParameters):
+    def __init__(self):
+        super().__init__()
+        self.max_size = (MemoryGranularity.Transitions, 100000)
+
+
+class NECAgentParameters(AgentParameters):
+    def __init__(self):
+        super().__init__(algorithm=NECAlgorithmParameters(),
+                         exploration=EGreedyParameters(),
+                         memory=NECMemoryParameters(),
+                         networks={"main": NECNetworkParameters()})
+        self.exploration.epsilon_schedule = ConstantSchedule(0.1)
+        self.exploration.evaluation_epsilon = 0.01
+
+    @property
+    def path(self):
+        return 'rl_coach.agents.nec_agent:NECAgent'
+
+
+# Neural Episodic Control - https://arxiv.org/pdf/1703.01988.pdf
+class NECAgent(ValueOptimizationAgent):
+    def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
+        super().__init__(agent_parameters, parent)
+        self.current_episode_state_embeddings = []
+        self.training_started = False
+        self.current_episode_buffer = \
+            Episode(discount=self.ap.algorithm.discount,
+                    n_step=self.ap.algorithm.n_step,
+                    bootstrap_total_return_from_old_policy=self.ap.algorithm.bootstrap_total_return_from_old_policy)
+
+    def learn_from_batch(self, batch):
+        if not self.networks['main'].online_network.output_heads[0].DND.has_enough_entries(self.ap.algorithm.number_of_knn):
+            return 0, [], 0
+        else:
+            if not self.training_started:
+                self.training_started = True
+                screen.log_title("Finished collecting initial entries in DND. Starting to train network...")
+
+        network_keys = self.ap.network_wrappers['main'].input_embedders_parameters.keys()
+
+        TD_targets = self.networks['main'].online_network.predict(batch.states(network_keys))
+
+        #  only update the action that we have actually done in this transition
+        for i in range(self.ap.network_wrappers['main'].batch_size):
+            TD_targets[i, batch.actions()[i]] = batch.total_returns()[i]
+
+        # set the gradients to fetch for the DND update
+        fetches = []
+        head = self.networks['main'].online_network.output_heads[0]
+        if self.ap.algorithm.propagate_updates_to_DND:
+            fetches = [head.dnd_embeddings_grad, head.dnd_values_grad, head.dnd_indices]
+
+        # train the neural network
+        result = self.networks['main'].train_and_sync_networks(batch.states(network_keys), TD_targets, fetches)
+
+        total_loss, losses, unclipped_grads = result[:3]
+
+        # update the DND keys and values using the extracted gradients
+        if self.ap.algorithm.propagate_updates_to_DND:
+            embedding_gradients = np.swapaxes(result[-1][0], 0, 1)
+            value_gradients = np.swapaxes(result[-1][1], 0, 1)
+            indices = np.swapaxes(result[-1][2], 0, 1)
+            head.DND.update_keys_and_values(batch.actions(), embedding_gradients, value_gradients, indices)
+
+        return total_loss, losses, unclipped_grads
+
+    def act(self):
+        if self.phase == RunPhase.HEATUP:
+            # get embedding in heatup (otherwise we get it through get_prediction)
+            embedding = self.networks['main'].online_network.predict(
+                self.prepare_batch_for_inference(self.curr_state, 'main'),
+                outputs=self.networks['main'].online_network.state_embedding)
+            self.current_episode_state_embeddings.append(embedding)
+
+        return super().act()
+
+    def get_all_q_values_for_states(self, states: StateType):
+        # we need to store the state embeddings regardless if the action is random or not
+        return self.get_prediction(states)
+
+    def get_prediction(self, states):
+        # get the actions q values and the state embedding
+        embedding, actions_q_values = self.networks['main'].online_network.predict(
+            self.prepare_batch_for_inference(states, 'main'),
+            outputs=[self.networks['main'].online_network.state_embedding,
+                     self.networks['main'].online_network.output_heads[0].output]
+        )
+        if self.phase != RunPhase.TEST:
+            # store the state embedding for inserting it to the DND later
+            self.current_episode_state_embeddings.append(embedding.squeeze())
+        actions_q_values = actions_q_values[0][0]
+        return actions_q_values
+
+    def reset_internal_state(self):
+        super().reset_internal_state()
+        self.current_episode_state_embeddings = []
+        self.current_episode_buffer = \
+            Episode(discount=self.ap.algorithm.discount,
+                    n_step=self.ap.algorithm.n_step,
+                    bootstrap_total_return_from_old_policy=self.ap.algorithm.bootstrap_total_return_from_old_policy)
+
+    def handle_episode_ended(self):
+        super().handle_episode_ended()
+
+        # get the last full episode that we have collected
+        episode = self.call_memory('get_last_complete_episode')
+        if episode is not None and self.phase != RunPhase.TEST:
+            assert len(self.current_episode_state_embeddings) == episode.length()
+            returns = episode.get_transitions_attribute('total_return')
+            actions = episode.get_transitions_attribute('action')
+            self.networks['main'].online_network.output_heads[0].DND.add(self.current_episode_state_embeddings,
+                                                                         actions, returns)
+
+    def save_checkpoint(self, checkpoint_id):
+        with open(os.path.join(self.ap.task_parameters.save_checkpoint_dir, str(checkpoint_id) + '.dnd'), 'wb') as f:
+            pickle.dump(self.networks['main'].online_network.output_heads[0].DND, f, pickle.HIGHEST_PROTOCOL)