#
# 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
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# 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.
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from copy import copy
from typing import Union
import numpy as np
from rl_coach.agents.dqn_agent import DQNAgentParameters, DQNNetworkParameters, DQNAlgorithmParameters
from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent
from rl_coach.architectures.head_parameters import QuantileRegressionQHeadParameters
from rl_coach.core_types import StateType
from rl_coach.schedules import LinearSchedule
class QuantileRegressionDQNNetworkParameters(DQNNetworkParameters):
def __init__(self):
super().__init__()
self.heads_parameters = [QuantileRegressionQHeadParameters()]
self.learning_rate = 0.00005
self.optimizer_epsilon = 0.01 / 32
[docs]class QuantileRegressionDQNAlgorithmParameters(DQNAlgorithmParameters):
"""
:param atoms: (int)
the number of atoms to predict for each action
:param huber_loss_interval: (float)
One of the huber loss parameters, and is referred to as :math:`\kapa` in the paper.
It describes the interval [-k, k] in which the huber loss acts as a MSE loss.
"""
def __init__(self):
super().__init__()
self.atoms = 200
self.huber_loss_interval = 1 # called k in the paper
class QuantileRegressionDQNAgentParameters(DQNAgentParameters):
def __init__(self):
super().__init__()
self.algorithm = QuantileRegressionDQNAlgorithmParameters()
self.network_wrappers = {"main": QuantileRegressionDQNNetworkParameters()}
self.exploration.epsilon_schedule = LinearSchedule(1, 0.01, 1000000)
self.exploration.evaluation_epsilon = 0.001
@property
def path(self):
return 'rl_coach.agents.qr_dqn_agent:QuantileRegressionDQNAgent'
# Quantile Regression Deep Q Network - https://arxiv.org/pdf/1710.10044v1.pdf
class QuantileRegressionDQNAgent(ValueOptimizationAgent):
def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
super().__init__(agent_parameters, parent)
self.quantile_probabilities = np.ones(self.ap.algorithm.atoms) / float(self.ap.algorithm.atoms)
def get_q_values(self, quantile_values):
return np.dot(quantile_values, self.quantile_probabilities)
# prediction's format is (batch,actions,atoms)
def get_all_q_values_for_states(self, states: StateType):
if self.exploration_policy.requires_action_values():
quantile_values = self.get_prediction(states)
actions_q_values = self.get_q_values(quantile_values)
else:
actions_q_values = None
return actions_q_values
# prediction's format is (batch,actions,atoms)
def get_all_q_values_for_states_and_softmax_probabilities(self, states: StateType):
actions_q_values, softmax_probabilities = None, None
if self.exploration_policy.requires_action_values():
outputs = copy(self.networks['main'].online_network.outputs)
outputs.append(self.networks['main'].online_network.output_heads[0].softmax)
quantile_values, softmax_probabilities = self.get_prediction(states, outputs)
actions_q_values = self.get_q_values(quantile_values)
return actions_q_values, softmax_probabilities
def learn_from_batch(self, batch):
network_keys = self.ap.network_wrappers['main'].input_embedders_parameters.keys()
# get the quantiles of the next states and current states
next_state_quantiles, current_quantiles = self.networks['main'].parallel_prediction([
(self.networks['main'].target_network, batch.next_states(network_keys)),
(self.networks['main'].online_network, batch.states(network_keys))
])
# add Q value samples for logging
self.q_values.add_sample(self.get_q_values(current_quantiles))
# 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(batch.size))
TD_targets = batch.rewards(True) + (1.0 - batch.game_overs(True)) * self.ap.algorithm.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, batch.actions())]
# calculate the cumulative quantile probabilities and reorder them to fit the sorted quantiles order
cumulative_probabilities = np.array(range(self.ap.algorithm.atoms + 1)) / float(self.ap.algorithm.atoms) # tau_i
quantile_midpoints = 0.5*(cumulative_probabilities[1:] + cumulative_probabilities[:-1]) # tau^hat_i
quantile_midpoints = np.tile(quantile_midpoints, (batch.size, 1))
sorted_quantiles = np.argsort(current_quantiles[batch_idx, batch.actions()])
for idx in range(batch.size):
quantile_midpoints[idx, :] = quantile_midpoints[idx, sorted_quantiles[idx]]
# train
result = self.networks['main'].train_and_sync_networks({
**batch.states(network_keys),
'output_0_0': actions_locations,
'output_0_1': quantile_midpoints,
}, TD_targets)
total_loss, losses, unclipped_grads = result[:3]
return total_loss, losses, unclipped_grads