coach v0.8.0

agents/actor_critic_agent.py

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+#
+# 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 agents.policy_optimization_agent import *
+from logger import *
+from utils import *
+import scipy.signal
+
+
+# Actor Critic - https://arxiv.org/abs/1602.01783
+class ActorCriticAgent(PolicyOptimizationAgent):
+    def __init__(self, env, tuning_parameters, replicated_device=None, thread_id=0, create_target_network = False):
+        PolicyOptimizationAgent.__init__(self, env, tuning_parameters, replicated_device, thread_id, create_target_network)
+        self.last_gradient_update_step_idx = 0
+        self.action_advantages = Signal('Advantages')
+        self.state_values = Signal('Values')
+        self.unclipped_grads = Signal('Grads (unclipped)')
+        self.signals.append(self.action_advantages)
+        self.signals.append(self.state_values)
+        self.signals.append(self.unclipped_grads)
+
+    # 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.tp.agent.discount * values[1:] - values[:-1]
+        gae = self.discount(deltas, self.tp.agent.discount * self.tp.agent.gae_lambda)
+
+        if self.tp.agent.estimate_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.tp.agent.discount)), 1)[:-1]
+        return gae, discounted_returns
+
+    def learn_from_batch(self, batch):
+        # batch contains a list of episodes to learn from
+        current_states, next_states, actions, rewards, game_overs, _ = self.extract_batch(batch)
+
+        # get the values for the current states
+        result = self.main_network.online_network.predict(current_states)
+        current_state_values = result[0]
+        self.state_values.add_sample(current_state_values)
+
+        # the targets for the state value estimator
+        num_transitions = len(game_overs)
+        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 game_overs[-1]:
+                R = 0
+            else:
+                R = self.main_network.online_network.predict(np.expand_dims(next_states[-1], 0))[0]
+
+            for i in reversed(range(num_transitions)):
+                R = rewards[i] + self.tp.agent.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.main_network.online_network.predict(np.expand_dims(next_states[-1], 0))[0]
+            values = np.append(current_state_values, bootstrapped_value)
+            if game_overs[-1]:
+                values[-1] = 0
+
+            # get general discounted returns table
+            gae_values, state_value_head_targets = self.get_general_advantage_estimation_values(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)
+        if not self.env.discrete_controls and len(actions.shape) < 2:
+            actions = np.expand_dims(actions, -1)
+
+        # train
+        result = self.main_network.online_network.accumulate_gradients([current_states, 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)
+        logger.create_signal_value('Value Loss', losses[0])
+        logger.create_signal_value('Policy Loss', losses[1])
+
+        return total_loss
+
+    def choose_action(self, curr_state, phase=RunPhase.TRAIN):
+        # convert to batch so we can run it through the network
+        observation = np.expand_dims(np.array(curr_state['observation']), 0)
+        if self.env.discrete_controls:
+            # DISCRETE
+            state_value, action_probabilities = self.main_network.online_network.predict(observation)
+            action_probabilities = action_probabilities.squeeze()
+            if phase == RunPhase.TRAIN:
+                action = self.exploration_policy.get_action(action_probabilities)
+            else:
+                action = np.argmax(action_probabilities)
+            action_info = {"action_probability": action_probabilities[action], "state_value": state_value}
+            self.entropy.add_sample(-np.sum(action_probabilities * np.log(action_probabilities)))
+        else:
+            # CONTINUOUS
+            state_value, action_values_mean, action_values_std = self.main_network.online_network.predict(observation)
+            action_values_mean = action_values_mean.squeeze()
+            action_values_std = action_values_std.squeeze()
+            if phase == RunPhase.TRAIN:
+                action = np.squeeze(np.random.randn(1, self.action_space_size) * action_values_std + action_values_mean)
+            else:
+                action = action_values_mean
+            action_info = {"action_probability": action, "state_value": state_value}
+
+        return action, action_info