mirror of
https://github.com/gryf/coach.git
synced 2025-12-17 19:20:19 +01:00
113 lines
4.9 KiB
Python
113 lines
4.9 KiB
Python
#
|
|
# 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 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
|
|
|
|
|
|
class QuantileRegressionDQNAlgorithmParameters(DQNAlgorithmParameters):
|
|
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
|
|
|
|
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))
|
|
])
|
|
|
|
# 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.ap.network_wrappers['main'].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, (self.ap.network_wrappers['main'].batch_size, 1))
|
|
sorted_quantiles = np.argsort(current_quantiles[batch_idx, batch.actions()])
|
|
for idx in range(self.ap.network_wrappers['main'].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
|
|
|