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categorical_dqn_agent.py
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categorical_dqn_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 typing import Union
import numpy as np
from rl_coach.agents.dqn_agent import DQNNetworkParameters, DQNAlgorithmParameters, DQNAgentParameters
from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent
from rl_coach.architectures.head_parameters import CategoricalQHeadParameters
from rl_coach.core_types import StateType
from rl_coach.exploration_policies.e_greedy import EGreedyParameters
from rl_coach.memories.non_episodic.prioritized_experience_replay import PrioritizedExperienceReplay
from rl_coach.schedules import LinearSchedule
class CategoricalDQNNetworkParameters(DQNNetworkParameters):
def __init__(self):
super().__init__()
self.heads_parameters = [CategoricalQHeadParameters()]
class CategoricalDQNAlgorithmParameters(DQNAlgorithmParameters):
"""
:param v_min: (float)
The minimal value that will be represented in the network output for predicting the Q value.
Corresponds to :math:`v_{min}` in the paper.
:param v_max: (float)
The maximum value that will be represented in the network output for predicting the Q value.
Corresponds to :math:`v_{max}` in the paper.
:param atoms: (int)
The number of atoms that will be used to discretize the range between v_min and v_max.
For the C51 algorithm described in the paper, the number of atoms is 51.
"""
def __init__(self):
super().__init__()
self.v_min = -10.0
self.v_max = 10.0
self.atoms = 51
class CategoricalDQNExplorationParameters(EGreedyParameters):
def __init__(self):
super().__init__()
self.epsilon_schedule = LinearSchedule(1, 0.01, 1000000)
self.evaluation_epsilon = 0.001
class CategoricalDQNAgentParameters(DQNAgentParameters):
def __init__(self):
super().__init__()
self.algorithm = CategoricalDQNAlgorithmParameters()
self.exploration = CategoricalDQNExplorationParameters()
self.network_wrappers = {"main": CategoricalDQNNetworkParameters()}
@property
def path(self):
return 'rl_coach.agents.categorical_dqn_agent:CategoricalDQNAgent'
# Categorical Deep Q Network - https://arxiv.org/pdf/1707.06887.pdf
class CategoricalDQNAgent(ValueOptimizationAgent):
def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
super().__init__(agent_parameters, parent)
self.z_values = np.linspace(self.ap.algorithm.v_min, self.ap.algorithm.v_max, self.ap.algorithm.atoms)
@property
def is_on_policy(self) -> bool:
return False
def distribution_prediction_to_q_values(self, prediction):
return np.dot(prediction, self.z_values)
# prediction's format is (batch,actions,atoms)
def get_all_q_values_for_states(self, states: StateType):
q_values = None
if self.exploration_policy.requires_action_values():
q_values = self.get_prediction(states,
outputs=[self.networks['main'].online_network.output_heads[0].q_values])
return q_values
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 = [self.networks['main'].online_network.output_heads[0].q_values,
self.networks['main'].online_network.output_heads[0].softmax]
actions_q_values, softmax_probabilities = self.get_prediction(states, outputs=outputs)
return actions_q_values, softmax_probabilities
def learn_from_batch(self, batch):
network_keys = self.ap.network_wrappers['main'].input_embedders_parameters.keys()
# for the action we actually took, the error is calculated by the atoms distribution
# for all other actions, the error is 0
distributional_q_st_plus_1, TD_targets = 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.distribution_prediction_to_q_values(TD_targets))
# select the optimal actions for the next state
target_actions = np.argmax(self.distribution_prediction_to_q_values(distributional_q_st_plus_1), axis=1)
m = np.zeros((batch.size, self.z_values.size))
batches = np.arange(batch.size)
# an alternative to the for loop. 3.7x perf improvement vs. the same code done with for looping.
# only 10% speedup overall - leaving commented out as the code is not as clear.
# tzj_ = np.fmax(np.fmin(batch.rewards() + (1.0 - batch.game_overs()) * self.ap.algorithm.discount *
# np.transpose(np.repeat(self.z_values[np.newaxis, :], batch.size, axis=0), (1, 0)),
# self.z_values[-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_ = np.zeros((batch.size, self.z_values.size))
# np.add.at(m_, [batches, l_],
# np.transpose(distributional_q_st_plus_1[batches, target_actions], (1, 0)) * (u_ - bj_))
# np.add.at(m_, [batches, u_],
# np.transpose(distributional_q_st_plus_1[batches, target_actions], (1, 0)) * (bj_ - l_))
for j in range(self.z_values.size):
tzj = np.fmax(np.fmin(batch.rewards() +
(1.0 - batch.game_overs()) * self.ap.algorithm.discount * self.z_values[j],
self.z_values[-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] += (distributional_q_st_plus_1[batches, target_actions, j] * (u - bj))
m[batches, u] += (distributional_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
# only update the action that we have actually done in this transition
TD_targets[batches, batch.actions()] = m
# update errors in prioritized replay buffer
importance_weights = batch.info('weight') if isinstance(self.memory, PrioritizedExperienceReplay) else None
result = self.networks['main'].train_and_sync_networks(batch.states(network_keys), TD_targets,
importance_weights=importance_weights)
total_loss, losses, unclipped_grads = result[:3]
# TODO: fix this spaghetti code
if isinstance(self.memory, PrioritizedExperienceReplay):
errors = losses[0][np.arange(batch.size), batch.actions()]
self.call_memory('update_priorities', (batch.info('idx'), errors))
return total_loss, losses, unclipped_grads