Release 0.9

Main changes are detailed below:

New features -
* CARLA 0.7 simulator integration
* Human control of the game play
* Recording of human game play and storing / loading the replay buffer
* Behavioral cloning agent and presets
* Golden tests for several presets
* Selecting between deep / shallow image embedders
* Rendering through pygame (with some boost in performance)

API changes -
* Improved environment wrapper API
* Added an evaluate flag to allow convenient evaluation of existing checkpoints
* Improve frameskip definition in Gym

Bug fixes -
* Fixed loading of checkpoints for agents with more than one network
* Fixed the N Step Q learning agent python3 compatibility

agents/distributional_dqn_agent.py

@@ -0,0 +1,60 @@
+#
+# 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.value_optimization_agent import *
+
+
+# Distributional Deep Q Network - https://arxiv.org/pdf/1707.06887.pdf
+class DistributionalDQNAgent(ValueOptimizationAgent):
+    def __init__(self, env, tuning_parameters, replicated_device=None, thread_id=0):
+        ValueOptimizationAgent.__init__(self, env, tuning_parameters, replicated_device, thread_id)
+        self.z_values = np.linspace(self.tp.agent.v_min, self.tp.agent.v_max, self.tp.agent.atoms)
+
+    # prediction's format is (batch,actions,atoms)
+    def get_q_values(self, prediction):
+        return np.dot(prediction, self.z_values)
+
+    def learn_from_batch(self, batch):
+        current_states, next_states, actions, rewards, game_overs, _ = self.extract_batch(batch)
+
+        # for the action we actually took, the error is calculated by the atoms distribution
+        # for all other actions, the error is 0
+        distributed_q_st_plus_1 = self.main_network.target_network.predict(next_states)
+        # initialize with the current prediction so that we will
+        TD_targets = self.main_network.online_network.predict(current_states)
+
+        # only update the action that we have actually done in this transition
+        target_actions = np.argmax(self.get_q_values(distributed_q_st_plus_1), axis=1)
+        m = np.zeros((self.tp.batch_size, self.z_values.size))
+
+        batches = np.arange(self.tp.batch_size)
+        for j in range(self.z_values.size):
+            tzj = np.fmax(np.fmin(rewards + (1.0 - game_overs) * self.tp.agent.discount * self.z_values[j],
+                                self.z_values[self.z_values.size - 1]),
+                                self.z_values[0])
+            bj = (tzj - self.z_values[0])/(self.z_values[1] - self.z_values[0])
+            u = (np.ceil(bj)).astype(int)
+            l = (np.floor(bj)).astype(int)
+            m[batches, l] = m[batches, l] + (distributed_q_st_plus_1[batches, target_actions, j] * (u - bj))
+            m[batches, u] = m[batches, u] + (distributed_q_st_plus_1[batches, target_actions, j] * (bj - l))
+        # total_loss = cross entropy between actual result above and predicted result for the given action
+        TD_targets[batches, actions] = m
+
+        result = self.main_network.train_and_sync_networks(current_states, TD_targets)
+        total_loss = result[0]
+
+        return total_loss
+