new feature - implementation of Quantile Regression DQN (https://arxiv.org/pdf/1710.10044v1.pdf)

API change - Distributional DQN renamed to Categorical DQN

README.md

@@ -195,7 +195,8 @@ python3 coach.py -p Hopper_A3C -n 16
 * [Dueling Q Network](https://arxiv.org/abs/1511.06581)
 * [Mixed Monte Carlo (MMC)](https://arxiv.org/abs/1703.01310)
 * [Persistent Advantage Learning (PAL)](https://arxiv.org/abs/1512.04860)
-* [Distributional Deep Q Network ](https://arxiv.org/abs/1707.06887)
+* [Categorical Deep Q Network (C51)](https://arxiv.org/abs/1707.06887)
+* [Quantile Regression Deep Q Network (QR-DQN)](https://arxiv.org/pdf/1710.10044v1.pdf)
 * [Bootstrapped Deep Q Network](https://arxiv.org/abs/1602.04621)
 * [N-Step Q Learning](https://arxiv.org/abs/1602.01783) | **Distributed**
 * [Neural Episodic Control (NEC) ](https://arxiv.org/abs/1703.01988)

agents/__init__.py

@@ -22,7 +22,7 @@ from agents.ddpg_agent import *
 from agents.ddqn_agent import *
 from agents.dfp_agent import *
 from agents.dqn_agent import *
-from agents.distributional_dqn_agent import *
+from agents.categorical_dqn_agent import *
 from agents.mmc_agent import *
 from agents.n_step_q_agent import *
 from agents.naf_agent import *
@@ -32,3 +32,4 @@ from agents.policy_gradients_agent import *
 from agents.policy_optimization_agent import *
 from agents.ppo_agent import *
 from agents.value_optimization_agent import *
+from agents.qr_dqn_agent import *

agents/categorical_dqn_agent.py

@@ -17,8 +17,8 @@
 from agents.value_optimization_agent import *
 
 
-# Distributional Deep Q Network - https://arxiv.org/pdf/1707.06887.pdf
+# Categorical Deep Q Network - https://arxiv.org/pdf/1707.06887.pdf
-class DistributionalDQNAgent(ValueOptimizationAgent):
+class CategoricalDQNAgent(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)

agents/qr_dqn_agent.py

@@ -0,0 +1,62 @@
+#
+# 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 *
+
+
+# Quantile Regression Deep Q Network - https://arxiv.org/pdf/1710.10044v1.pdf
+class QuantileRegressionDQNAgent(ValueOptimizationAgent):
+    def __init__(self, env, tuning_parameters, replicated_device=None, thread_id=0):
+        ValueOptimizationAgent.__init__(self, env, tuning_parameters, replicated_device, thread_id)
+        self.quantile_probabilities = np.ones(self.tp.agent.atoms) / float(self.tp.agent.atoms)
+
+    # prediction's format is (batch,actions,atoms)
+    def get_q_values(self, quantile_values):
+        return np.dot(quantile_values, self.quantile_probabilities)
+
+    def learn_from_batch(self, batch):
+        current_states, next_states, actions, rewards, game_overs, _ = self.extract_batch(batch)
+
+        # get the quantiles of the next states and current states
+        next_state_quantiles = self.main_network.target_network.predict(next_states)
+        current_quantiles = self.main_network.online_network.predict(current_states)
+
+        # get the optimal actions to take for the next states
+        target_actions = np.argmax(self.get_q_values(next_state_quantiles), axis=1)
+
+        # calculate the Bellman update
+        batch_idx = list(range(self.tp.batch_size))
+        rewards = np.expand_dims(rewards, -1)
+        game_overs = np.expand_dims(game_overs, -1)
+        TD_targets = rewards + (1.0 - game_overs) * self.tp.agent.discount \
+                               * next_state_quantiles[batch_idx, target_actions]
+
+        # get the locations of the selected actions within the batch for indexing purposes
+        actions_locations = [[b, a] for b, a in zip(batch_idx, actions)]
+
+        # calculate the cumulative quantile probabilities and reorder them to fit the sorted quantiles order
+        cumulative_probabilities = np.array(range(self.tp.agent.atoms+1))/float(self.tp.agent.atoms)  # tau_i
+        quantile_midpoints = 0.5*(cumulative_probabilities[1:] + cumulative_probabilities[:-1])  # tau^hat_i
+        quantile_midpoints = np.tile(quantile_midpoints, (self.tp.batch_size, 1))
+        for idx in range(self.tp.batch_size):
+            quantile_midpoints[idx, :] = quantile_midpoints[idx, np.argsort(current_quantiles[batch_idx, actions])[idx]]
+
+        # train
+        result = self.main_network.train_and_sync_networks([current_states, actions_locations, quantile_midpoints], TD_targets)
+        total_loss = result[0]
+
+        return total_loss
+

architectures/tensorflow_components/general_network.py

@@ -80,7 +80,8 @@ class GeneralTensorFlowNetwork(TensorFlowArchitecture):
             OutputTypes.NAF: NAFHead,
             OutputTypes.PPO: PPOHead,
             OutputTypes.PPO_V : PPOVHead,
-            OutputTypes.DistributionalQ: DistributionalQHead
+            OutputTypes.CategoricalQ: CategoricalQHead,
+            OutputTypes.QuantileRegressionQ: QuantileRegressionQHead
         }
         return output_mapping[head_type](self.tp, head_idx, loss_weight, self.network_is_local)
 

architectures/tensorflow_components/heads.py

@@ -462,10 +462,10 @@ class PPOVHead(Head):
         tf.losses.add_loss(self.loss)
 
 
-class DistributionalQHead(Head):
+class CategoricalQHead(Head):
     def __init__(self, tuning_parameters, head_idx=0, loss_weight=1., is_local=True):
         Head.__init__(self, tuning_parameters, head_idx, loss_weight, is_local)
-        self.name = 'distributional_dqn_head'
+        self.name = 'categorical_dqn_head'
         self.num_actions = tuning_parameters.env_instance.action_space_size
         self.num_atoms = tuning_parameters.agent.atoms
 
@@ -484,3 +484,47 @@ class DistributionalQHead(Head):
         self.loss = tf.nn.softmax_cross_entropy_with_logits(labels=self.target, logits=values_distribution)
         tf.losses.add_loss(self.loss)
 
+
+class QuantileRegressionQHead(Head):
+    def __init__(self, tuning_parameters, head_idx=0, loss_weight=1., is_local=True):
+        Head.__init__(self, tuning_parameters, head_idx, loss_weight, is_local)
+        self.name = 'quantile_regression_dqn_head'
+        self.num_actions = tuning_parameters.env_instance.action_space_size
+        self.num_atoms = tuning_parameters.agent.atoms  # we use atom / quantile interchangeably
+        self.huber_loss_interval = 1  # k
+
+    def _build_module(self, input_layer):
+        self.actions = tf.placeholder(tf.int32, [None, 2], name="actions")
+        self.quantile_midpoints = tf.placeholder(tf.float32, [None, self.num_atoms], name="quantile_midpoints")
+        self.input = [self.actions, self.quantile_midpoints]
+
+        # the output of the head is the N unordered quantile locations {theta_1, ..., theta_N}
+        quantiles_locations = tf.layers.dense(input_layer, self.num_actions * self.num_atoms)
+        quantiles_locations = tf.reshape(quantiles_locations, (tf.shape(quantiles_locations)[0], self.num_actions, self.num_atoms))
+        self.output = quantiles_locations
+
+        self.quantiles = tf.placeholder(tf.float32, shape=(None, self.num_atoms), name="quantiles")
+        self.target = self.quantiles
+
+        # only the quantiles of the taken action are taken into account
+        quantiles_for_used_actions = tf.gather_nd(quantiles_locations, self.actions)
+
+        # reorder the output quantiles and the target quantiles as a preparation step for calculating the loss
+        # the output quantiles vector and the quantile midpoints are tiled as rows of a NxN matrix (N = num quantiles)
+        # the target quantiles vector is tiled as column of a NxN matrix
+        theta_i = tf.tile(tf.expand_dims(quantiles_for_used_actions, -1), [1, 1, self.num_atoms])
+        T_theta_j = tf.tile(tf.expand_dims(self.target, -2), [1, self.num_atoms, 1])
+        tau_i = tf.tile(tf.expand_dims(self.quantile_midpoints, -1), [1, 1, self.num_atoms])
+
+        # Huber loss of T(theta_j) - theta_i
+        abs_error = tf.abs(T_theta_j - theta_i)
+        quadratic = tf.minimum(abs_error, self.huber_loss_interval)
+        huber_loss = self.huber_loss_interval * (abs_error - quadratic) + 0.5 * quadratic ** 2
+
+        # Quantile Huber loss
+        quantile_huber_loss = tf.abs(tau_i - tf.cast(abs_error < 0, dtype=tf.float32)) * huber_loss
+
+        # Quantile regression loss (the probability for each quantile is 1/num_quantiles)
+        quantile_regression_loss = tf.reduce_sum(quantile_huber_loss) / float(self.num_atoms)
+        self.loss = quantile_regression_loss
+        tf.losses.add_loss(self.loss)

configurations.py

@@ -42,7 +42,8 @@ class OutputTypes(object):
     NAF = 7
     PPO = 8
     PPO_V = 9
-    DistributionalQ = 10
+    CategoricalQ = 10
+    QuantileRegressionQ = 11
 
 
 class MiddlewareTypes(object):
@@ -307,14 +308,20 @@ class BootstrappedDQN(DQN):
     num_output_head_copies = 10
 
 
-class DistributionalDQN(DQN):
+class CategoricalDQN(DQN):
-    type = 'DistributionalDQNAgent'
+    type = 'CategoricalDQNAgent'
-    output_types = [OutputTypes.DistributionalQ]
+    output_types = [OutputTypes.CategoricalQ]
     v_min = -10.0
     v_max = 10.0
     atoms = 51
 
 
+class QuantileRegressionDQN(DQN):
+    type = 'QuantileRegressionDQNAgent'
+    output_types = [OutputTypes.QuantileRegressionQ]
+    atoms = 51
+
+
 class NEC(AgentParameters):
     type = 'NECAgent'
     optimizer_type = 'RMSProp'

docs/docs/algorithms/value_optimization/categorical_dqn.md

@@ -1,4 +1,4 @@
-# Distributional DQN
+# Categorical DQN
 
 **Actions space:** Discrete
 

docs/mkdocs.yml

@@ -15,7 +15,7 @@ pages:
         - 'DQN' : algorithms/value_optimization/dqn.md
         - 'Double DQN' : algorithms/value_optimization/double_dqn.md
         - 'Dueling DQN' : algorithms/value_optimization/dueling_dqn.md
-        - 'Distributional DQN' : algorithms/value_optimization/distributional_dqn.md
+        - 'Categorical DQN' : algorithms/value_optimization/categorical_dqn.md
         - 'Mixed Monte Carlo' : algorithms/value_optimization/mmc.md
         - 'Persistent Advantage Learning' : algorithms/value_optimization/pal.md
         - 'Neural Episodic Control' : algorithms/value_optimization/nec.md

presets.py

@@ -70,6 +70,18 @@ class Doom_Basic_DQN(Preset):
         self.num_heatup_steps = 1000
 
 
+
+class Doom_Basic_QRDQN(Preset):
+    def __init__(self):
+        Preset.__init__(self, QuantileRegressionDQN, Doom, ExplorationParameters)
+        self.env.level = 'basic'
+        self.agent.num_steps_between_copying_online_weights_to_target = 1000
+        self.learning_rate = 0.00025
+        self.agent.num_episodes_in_experience_replay = 200
+        self.num_heatup_steps = 1000
+
+
+
 class Doom_Basic_OneStepQ(Preset):
     def __init__(self):
         Preset.__init__(self, NStepQ, Doom, ExplorationParameters)
@@ -340,9 +352,9 @@ class CartPole_DQN(Preset):
         self.test_min_return_threshold = 150
 
 
-class CartPole_DistributionalDQN(Preset):
+class CartPole_C51(Preset):
     def __init__(self):
-        Preset.__init__(self, DistributionalDQN, GymVectorObservation, ExplorationParameters)
+        Preset.__init__(self, CategoricalDQN, GymVectorObservation, ExplorationParameters)
         self.env.level = 'CartPole-v0'
         self.agent.num_steps_between_copying_online_weights_to_target = 100
         self.learning_rate = 0.00025
@@ -356,6 +368,18 @@ class CartPole_DistributionalDQN(Preset):
         self.test_min_return_threshold = 150
 
 
+class CartPole_QRDQN(Preset):
+    def __init__(self):
+        Preset.__init__(self, QuantileRegressionDQN, GymVectorObservation, ExplorationParameters)
+        self.env.level = 'CartPole-v0'
+        self.agent.num_steps_between_copying_online_weights_to_target = 100
+        self.learning_rate = 0.00025
+        self.agent.num_episodes_in_experience_replay = 200
+        self.num_heatup_steps = 1000
+        self.exploration.epsilon_decay_steps = 3000
+        self.agent.discount = 1.0
+
+
 # The below preset matches the hyper-parameters setting as in the original DQN paper.
 # This a very resource intensive preset, and might easily blow up your RAM (> 100GB of usage).
 # Try reducing the number of transitions in the experience replay (50e3 might be a reasonable number to start with),
@@ -377,9 +401,9 @@ class Breakout_DQN(Preset):
         self.evaluate_every_x_episodes = 100
 
 
-class Breakout_DistributionalDQN(Preset):
+class Breakout_C51(Preset):
     def __init__(self):
-        Preset.__init__(self, DistributionalDQN, Atari, ExplorationParameters)
+        Preset.__init__(self, CategoricalDQN, Atari, ExplorationParameters)
         self.env.level = 'BreakoutDeterministic-v4'
         self.agent.num_steps_between_copying_online_weights_to_target = 10000
         self.learning_rate = 0.00025