pre-release 0.10.0

rl_coach/agents/actor_critic_agent.py

@@ -0,0 +1,165 @@
+#
+# 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
+import scipy.signal
+from rl_coach.agents.policy_optimization_agent import PolicyOptimizationAgent, PolicyGradientRescaler
+from rl_coach.architectures.tensorflow_components.heads.policy_head import PolicyHeadParameters
+from rl_coach.architectures.tensorflow_components.heads.v_head import VHeadParameters
+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 QActionStateValue
+from rl_coach.spaces import DiscreteActionSpace
+from rl_coach.utils import last_sample
+
+from rl_coach.logger import screen
+from rl_coach.memories.episodic.single_episode_buffer import SingleEpisodeBufferParameters
+
+
+class ActorCriticAlgorithmParameters(AlgorithmParameters):
+    def __init__(self):
+        super().__init__()
+        self.policy_gradient_rescaler = PolicyGradientRescaler.A_VALUE
+        self.apply_gradients_every_x_episodes = 5
+        self.beta_entropy = 0
+        self.num_steps_between_gradient_updates = 5000  # this is called t_max in all the papers
+        self.gae_lambda = 0.96
+        self.estimate_state_value_using_gae = False
+
+
+class ActorCriticNetworkParameters(NetworkParameters):
+    def __init__(self):
+        super().__init__()
+        self.input_embedders_parameters = {'observation': InputEmbedderParameters()}
+        self.middleware_parameters = FCMiddlewareParameters()
+        self.heads_parameters = [VHeadParameters(), PolicyHeadParameters()]
+        self.loss_weights = [0.5, 1.0]
+        self.rescale_gradient_from_head_by_factor = [1, 1]
+        self.optimizer_type = 'Adam'
+        self.clip_gradients = 40.0
+        self.async_training = True
+
+
+class ActorCriticAgentParameters(AgentParameters):
+    def __init__(self):
+        super().__init__(algorithm=ActorCriticAlgorithmParameters(),
+                         exploration=None, #TODO this should be different for continuous (ContinuousEntropyExploration)
+                                           #  and discrete (CategoricalExploration) action spaces.
+                         memory=SingleEpisodeBufferParameters(),
+                         networks={"main": ActorCriticNetworkParameters()})
+
+    @property
+    def path(self):
+        return 'rl_coach.agents.actor_critic_agent:ActorCriticAgent'
+
+
+# Actor Critic - https://arxiv.org/abs/1602.01783
+class ActorCriticAgent(PolicyOptimizationAgent):
+    def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
+        super().__init__(agent_parameters, parent)
+        self.last_gradient_update_step_idx = 0
+        self.action_advantages = self.register_signal('Advantages')
+        self.state_values = self.register_signal('Values')
+        self.value_loss = self.register_signal('Value Loss')
+        self.policy_loss = self.register_signal('Policy Loss')
+
+    # Discounting function used to calculate discounted returns.
+    def discount(self, x, gamma):
+        return scipy.signal.lfilter([1], [1, -gamma], x[::-1], axis=0)[::-1]
+
+    def get_general_advantage_estimation_values(self, rewards, values):
+        # values contain n+1 elements (t ... t+n+1), rewards contain n elements (t ... t + n)
+        bootstrap_extended_rewards = np.array(rewards.tolist() + [values[-1]])
+
+        # Approximation based calculation of GAE (mathematically correct only when Tmax = inf,
+        # although in practice works even in much smaller Tmax values, e.g. 20)
+        deltas = rewards + self.ap.algorithm.discount * values[1:] - values[:-1]
+        gae = self.discount(deltas, self.ap.algorithm.discount * self.ap.algorithm.gae_lambda)
+
+        if self.ap.algorithm.estimate_state_value_using_gae:
+            discounted_returns = np.expand_dims(gae + values[:-1], -1)
+        else:
+            discounted_returns = np.expand_dims(np.array(self.discount(bootstrap_extended_rewards,
+                                                                       self.ap.algorithm.discount)), 1)[:-1]
+        return gae, discounted_returns
+
+    def learn_from_batch(self, batch):
+        # batch contains a list of episodes to learn from
+        network_keys = self.ap.network_wrappers['main'].input_embedders_parameters.keys()
+
+        # get the values for the current states
+
+        result = self.networks['main'].online_network.predict(batch.states(network_keys))
+        current_state_values = result[0]
+
+        self.state_values.add_sample(current_state_values)
+
+        # the targets for the state value estimator
+        num_transitions = batch.size
+        state_value_head_targets = np.zeros((num_transitions, 1))
+
+        # estimate the advantage function
+        action_advantages = np.zeros((num_transitions, 1))
+
+        if self.policy_gradient_rescaler == PolicyGradientRescaler.A_VALUE:
+            if batch.game_overs()[-1]:
+                R = 0
+            else:
+                R = self.networks['main'].online_network.predict(last_sample(batch.next_states(network_keys)))[0]
+
+            for i in reversed(range(num_transitions)):
+                R = batch.rewards()[i] + self.ap.algorithm.discount * R
+                state_value_head_targets[i] = R
+                action_advantages[i] = R - current_state_values[i]
+
+        elif self.policy_gradient_rescaler == PolicyGradientRescaler.GAE:
+            # get bootstraps
+            bootstrapped_value = self.networks['main'].online_network.predict(last_sample(batch.next_states(network_keys)))[0]
+            values = np.append(current_state_values, bootstrapped_value)
+            if batch.game_overs()[-1]:
+                values[-1] = 0
+
+            # get general discounted returns table
+            gae_values, state_value_head_targets = self.get_general_advantage_estimation_values(batch.rewards(), values)
+            action_advantages = np.vstack(gae_values)
+        else:
+            screen.warning("WARNING: The requested policy gradient rescaler is not available")
+
+        action_advantages = action_advantages.squeeze(axis=-1)
+        actions = batch.actions()
+        if not isinstance(self.spaces.action, DiscreteActionSpace) and len(actions.shape) < 2:
+            actions = np.expand_dims(actions, -1)
+
+        # train
+        result = self.networks['main'].online_network.accumulate_gradients({**batch.states(network_keys),
+                                                                            'output_1_0': actions},
+                                                                       [state_value_head_targets, action_advantages])
+
+        # logging
+        total_loss, losses, unclipped_grads = result[:3]
+        self.action_advantages.add_sample(action_advantages)
+        self.unclipped_grads.add_sample(unclipped_grads)
+        self.value_loss.add_sample(losses[0])
+        self.policy_loss.add_sample(losses[1])
+
+        return total_loss, losses, unclipped_grads
+
+    def get_prediction(self, states):
+        tf_input_state = self.prepare_batch_for_inference(states, "main")
+        return self.networks['main'].online_network.predict(tf_input_state)[1:]  # index 0 is the state value