pre-release 0.10.0

rl_coach/agents/policy_gradients_agent.py

@@ -0,0 +1,105 @@
+#
+# 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.policy_optimization_agent import PolicyOptimizationAgent, PolicyGradientRescaler
+from rl_coach.architectures.tensorflow_components.heads.policy_head import PolicyHeadParameters
+from rl_coach.architectures.tensorflow_components.middlewares.fc_middleware import FCMiddlewareParameters
+from rl_coach.base_parameters import NetworkParameters, AlgorithmParameters, \
+    AgentParameters, InputEmbedderParameters
+from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters
+from rl_coach.spaces import DiscreteActionSpace
+
+from rl_coach.logger import screen
+from rl_coach.memories.episodic.single_episode_buffer import SingleEpisodeBufferParameters
+
+
+class PolicyGradientNetworkParameters(NetworkParameters):
+    def __init__(self):
+        super().__init__()
+        self.input_embedders_parameters = {'observation': InputEmbedderParameters()}
+        self.middleware_parameters = FCMiddlewareParameters()
+        self.heads_parameters = [PolicyHeadParameters()]
+        self.loss_weights = [1.0]
+        self.async_training = True
+
+
+class PolicyGradientAlgorithmParameters(AlgorithmParameters):
+    def __init__(self):
+        super().__init__()
+        self.policy_gradient_rescaler = PolicyGradientRescaler.FUTURE_RETURN_NORMALIZED_BY_TIMESTEP
+        self.apply_gradients_every_x_episodes = 5
+        self.beta_entropy = 0
+        self.num_steps_between_gradient_updates = 20000  # this is called t_max in all the papers
+
+
+class PolicyGradientsAgentParameters(AgentParameters):
+    def __init__(self):
+        super().__init__(algorithm=PolicyGradientAlgorithmParameters(),
+                         exploration=AdditiveNoiseParameters(),
+                         memory=SingleEpisodeBufferParameters(),
+                         networks={"main": PolicyGradientNetworkParameters()})
+
+    @property
+    def path(self):
+        return 'rl_coach.agents.policy_gradients_agent:PolicyGradientsAgent'
+
+
+class PolicyGradientsAgent(PolicyOptimizationAgent):
+    def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
+        super().__init__(agent_parameters, parent)
+        self.returns_mean = self.register_signal('Returns Mean')
+        self.returns_variance = self.register_signal('Returns Variance')
+        self.last_gradient_update_step_idx = 0
+
+    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()
+
+        total_returns = batch.total_returns()
+        for i in reversed(range(batch.size)):
+            if self.policy_gradient_rescaler == PolicyGradientRescaler.TOTAL_RETURN:
+                total_returns[i] = total_returns[0]
+            elif self.policy_gradient_rescaler == PolicyGradientRescaler.FUTURE_RETURN:
+                # just take the total return as it is
+                pass
+            elif self.policy_gradient_rescaler == PolicyGradientRescaler.FUTURE_RETURN_NORMALIZED_BY_EPISODE:
+                # we can get a single transition episode while playing Doom Basic, causing the std to be 0
+                if self.std_discounted_return != 0:
+                    total_returns[i] = (total_returns[i] - self.mean_discounted_return) / self.std_discounted_return
+                else:
+                    total_returns[i] = 0
+            elif self.policy_gradient_rescaler == PolicyGradientRescaler.FUTURE_RETURN_NORMALIZED_BY_TIMESTEP:
+                total_returns[i] -= self.mean_return_over_multiple_episodes[i]
+            else:
+                screen.warning("WARNING: The requested policy gradient rescaler is not available")
+
+        targets = total_returns
+        actions = batch.actions()
+        if type(self.spaces.action) != DiscreteActionSpace and len(actions.shape) < 2:
+            actions = np.expand_dims(actions, -1)
+
+        self.returns_mean.add_sample(np.mean(total_returns))
+        self.returns_variance.add_sample(np.std(total_returns))
+
+        result = self.networks['main'].online_network.accumulate_gradients(
+            {**batch.states(network_keys), 'output_0_0': actions}, targets
+        )
+        total_loss, losses, unclipped_grads = result[:3]
+
+        return total_loss, losses, unclipped_grads