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pg_log_prob_model.py
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pg_log_prob_model.py
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# Copyright 2018 Tensorforce Team. 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.
# ==============================================================================
import tensorflow as tf
from tensorforce import util
from tensorforce.core.models import PGModel
class PGLogProbModel(PGModel):
"""
Policy gradient model based on computing log likelihoods, e.g. the classical REINFORCE
algorithm.
"""
def tf_loss_per_instance(
self, states, internals, actions, terminal, reward, next_states, next_internals,
reference=None
):
embedding = self.network.apply(x=states, internals=internals)
log_probs = list()
for name, distribution in self.distributions.items():
parameters = distribution.parametrize(x=embedding)
action = actions[name]
log_prob = distribution.log_probability(parameters=parameters, action=action)
collapsed_size = util.product(xs=util.shape(log_prob)[1:])
log_prob = tf.reshape(tensor=log_prob, shape=(-1, collapsed_size))
log_probs.append(log_prob)
log_probs = tf.concat(values=log_probs, axis=1)
log_prob_per_instance = tf.reduce_mean(input_tensor=log_probs, axis=1)
return -log_prob_per_instance * reward