# 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 Model

class RandomModel(Model):

"""

Utility class to return random actions of a desired shape and with given bounds.

"""

def __init__(

self,

states,

actions,

scope,

device,

saver,

summarizer,

distributed,

batching_capacity

):

super(RandomModel, self).__init__(

states=states,

actions=actions,

scope=scope,

device=device,

saver=saver,

summarizer=summarizer,

distributed=distributed,

batching_capacity=batching_capacity,

variable_noise=None,

states_preprocessing=None,

actions_exploration=None,

reward_preprocessing=None

)

def tf_actions_and_internals(self, states, internals, deterministic):

assert len(internals) == 0

actions = dict()

for name, action in self.actions_spec.items():

shape = (tf.shape(input=next(iter(states.values())))[0],) + action['shape']

if action['type'] == 'bool':

actions[name] = (tf.random_uniform(shape=shape) < 0.5)

elif action['type'] == 'int':

actions[name] = tf.random_uniform(shape=shape, maxval=action['num_actions'], dtype=util.tf_dtype('int'))

elif action['type'] == 'float':

if 'min_value' in action:

actions[name] = tf.random_uniform(

shape=shape,

minval=action['min_value'],

maxval=action['max_value']

)

else:

actions[name] = tf.random_normal(shape=shape)

return actions, dict()

def tf_observe_timestep(self, states, internals, actions, terminal, reward):

return tf.no_op()