# 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 print_function

from __future__ import division

import tensorflow as tf

from tensorforce import util

from tensorforce.models import PGModel

class PGLogProbModel(PGModel):

"""

Policy gradient model based on computing log likelihoods, e.g. VPG.

"""

def tf_loss_per_instance(self, states, internals, actions, terminal, reward, next_states, next_internals, update, reference=None):

embedding = self.network.apply(x=states, internals=internals, update=update)

log_probs = list()

for name, distribution in self.distributions.items():

distr_params = distribution.parameterize(x=embedding)

log_prob = distribution.log_probability(distr_params=distr_params, action=actions[name])

collapsed_size = util.prod(util.shape(log_prob)[1:])

log_prob = tf.reshape(tensor=log_prob, shape=(-1, collapsed_size))

log_probs.append(log_prob)

log_prob = tf.reduce_mean(input_tensor=tf.concat(values=log_probs, axis=1), axis=1)

return -log_prob * reward