mirror of
https://github.com/gryf/coach.git
synced 2025-12-17 11:10:20 +01:00
1b095aeeca
Till now, most of the modules were importing all of the module objects (variables, classes, functions, other imports) into module namespace, which potentially could (and was) cause of unintentional use of class or methods, which was indirect imported. With this patch, all the star imports were substituted with top-level module, which provides desired class or function. Besides, all imports where sorted (where possible) in a way pep8[1] suggests - first are imports from standard library, than goes third party imports (like numpy, tensorflow etc) and finally coach modules. All of those sections are separated by one empty line. [1] https://www.python.org/dev/peps/pep-0008/#imports
68 lines
3.1 KiB
Python
68 lines
3.1 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.
|
|
#
|
|
import numpy as np
|
|
|
|
from agents import value_optimization_agent as voa
|
|
|
|
|
|
# Quantile Regression Deep Q Network - https://arxiv.org/pdf/1710.10044v1.pdf
|
|
class QuantileRegressionDQNAgent(voa.ValueOptimizationAgent):
|
|
def __init__(self, env, tuning_parameters, replicated_device=None, thread_id=0):
|
|
voa.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))
|
|
sorted_quantiles = np.argsort(current_quantiles[batch_idx, actions])
|
|
for idx in range(self.tp.batch_size):
|
|
quantile_midpoints[idx, :] = quantile_midpoints[idx, sorted_quantiles[idx]]
|
|
|
|
# train
|
|
result = self.main_network.train_and_sync_networks({
|
|
**current_states,
|
|
'output_0_0': actions_locations,
|
|
'output_0_1': quantile_midpoints,
|
|
}, TD_targets)
|
|
total_loss = result[0]
|
|
|
|
return total_loss
|