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# Copyright 2017 reinforce.io. All Rights Reserved.
#
# 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 __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from tensorforce import util
from tensorforce.models import QModel
class QNstepModel(QModel):
"""
Deep Q network using n-step rewards as described in Asynchronous Methods for Reinforcement Learning.
"""
def tf_q_delta(self, q_value, next_q_value, terminal, reward):
for _ in range(util.rank(q_value) - 1):
terminal = tf.expand_dims(input=terminal, axis=1)
reward = tf.expand_dims(input=reward, axis=1)
multiples = (1,) + util.shape(q_value)[1:]
terminal = tf.tile(input=terminal, multiples=multiples)
reward = tf.tile(input=reward, multiples=multiples)
reward = self.fn_discounted_cumulative_reward(
terminal=terminal,
reward=reward,
discount=self.discount,
final_reward=next_q_value[-1]
)
return reward - q_value
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