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bernoulli_thompson_sampling_agent_test.py
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bernoulli_thompson_sampling_agent_test.py
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# coding=utf-8
# Copyright 2020 The TF-Agents Authors.
#
# 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
#
# https://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.
"""Tests for bernoulli_thompson_sampling_agent.py."""
import numpy as np
import tensorflow as tf # pylint: disable=g-explicit-tensorflow-version-import
from tf_agents.bandits.agents import bernoulli_thompson_sampling_agent as bern_ts_agent
from tf_agents.bandits.drivers import driver_utils
from tf_agents.policies import utils as policy_utilities
from tf_agents.specs import tensor_spec
from tf_agents.trajectories import policy_step
from tf_agents.trajectories import time_step as ts
from tf_agents.utils import common
def _get_initial_and_final_steps(observations, rewards):
batch_size = tf.nest.flatten(observations)[0].shape[0]
if isinstance(observations, np.ndarray):
observations = tf.constant(
observations, dtype=tf.float32, name='observation'
)
initial_step = ts.TimeStep(
tf.constant(
ts.StepType.FIRST,
dtype=tf.int32,
shape=[batch_size],
name='step_type',
),
tf.constant(0.0, dtype=tf.float32, shape=[batch_size], name='reward'),
tf.constant(1.0, dtype=tf.float32, shape=[batch_size], name='discount'),
observations,
)
final_step = ts.TimeStep(
tf.constant(
ts.StepType.LAST, dtype=tf.int32, shape=[batch_size], name='step_type'
),
tf.constant(rewards, dtype=tf.float32, name='reward'),
tf.constant(1.0, dtype=tf.float32, shape=[batch_size], name='discount'),
observations,
)
return initial_step, final_step
def _get_initial_and_final_steps_with_action_mask(observations, rewards):
batch_size = tf.nest.flatten(observations)[0].shape[0]
initial_step = ts.TimeStep(
tf.constant(
ts.StepType.FIRST,
dtype=tf.int32,
shape=[batch_size],
name='step_type',
),
tf.constant(0.0, dtype=tf.float32, shape=[batch_size], name='reward'),
tf.constant(1.0, dtype=tf.float32, shape=[batch_size], name='discount'),
(observations[0], observations[1]),
)
final_step = ts.TimeStep(
tf.constant(
ts.StepType.LAST, dtype=tf.int32, shape=[batch_size], name='step_type'
),
tf.constant(rewards, dtype=tf.float32, name='reward'),
tf.constant(1.0, dtype=tf.float32, shape=[batch_size], name='discount'),
(
tf.nest.map_structure(lambda x: x + 100.0, observations[0]),
observations[1],
),
)
return initial_step, final_step
def _get_action_step(action):
return policy_step.PolicyStep(
action=tf.convert_to_tensor(action), info=policy_utilities.PolicyInfo()
)
def _get_experience(initial_step, action_step, final_step):
single_experience = driver_utils.trajectory_for_bandit(
initial_step, action_step, final_step
)
# Adds a 'time' dimension.
return tf.nest.map_structure(
lambda x: tf.expand_dims(tf.convert_to_tensor(x), 1), single_experience
)
class AgentTest(tf.test.TestCase):
def setUp(self):
super(AgentTest, self).setUp()
tf.compat.v1.enable_resource_variables()
self._obs_spec = tensor_spec.TensorSpec([2], tf.float32)
self._time_step_spec = ts.time_step_spec(self._obs_spec)
self._action_spec = tensor_spec.BoundedTensorSpec(
dtype=tf.int32, shape=(), minimum=0, maximum=2
)
self._num_actions = 3
self._observation_spec = self._time_step_spec.observation
def testCreateAgent(self):
agent = bern_ts_agent.BernoulliThompsonSamplingAgent(
self._time_step_spec, self._action_spec
)
self.assertIsNotNone(agent.policy)
def testInitializeAgent(self):
agent = bern_ts_agent.BernoulliThompsonSamplingAgent(
self._time_step_spec, self._action_spec
)
init_op = agent.initialize()
if not tf.executing_eagerly():
with self.cached_session() as sess:
common.initialize_uninitialized_variables(sess)
self.assertIsNone(sess.run(init_op))
def testPolicy(self):
agent = bern_ts_agent.BernoulliThompsonSamplingAgent(
self._time_step_spec, self._action_spec, batch_size=2
)
observations = tf.constant([[1, 1]], dtype=tf.float32)
time_steps = ts.restart(observations, batch_size=2)
policy = agent.policy
action_step = policy.action(time_steps)
# Batch size 2.
self.assertAllEqual([2], action_step.action.shape)
self.evaluate(tf.compat.v1.initialize_all_variables())
self.assertEqual(action_step.action.shape.as_list(), [2])
self.assertEqual(action_step.action.dtype, tf.int32)
def testTrainAgent(self):
observations = np.array([[1, 1]], dtype=np.float32)
actions = np.array([0, 1], dtype=np.int32)
rewards = np.array([0.0, 1.0], dtype=np.float32)
initial_step, final_step = _get_initial_and_final_steps(
observations, rewards
)
action_step = _get_action_step(actions)
experience = _get_experience(initial_step, action_step, final_step)
agent = bern_ts_agent.BernoulliThompsonSamplingAgent(
self._time_step_spec, self._action_spec, batch_size=2
)
init_op = agent.initialize()
if not tf.executing_eagerly():
with self.cached_session() as sess:
common.initialize_uninitialized_variables(sess)
self.assertIsNone(sess.run(init_op))
loss, _ = agent._train(experience, weights=None)
self.evaluate(tf.compat.v1.initialize_all_variables())
# The loss is -sum(rewards).
self.assertAllClose(self.evaluate(loss), -1.0)
def testTrainAgentWithMask(self):
time_step_spec = ts.time_step_spec((
tensor_spec.TensorSpec([], tf.float32),
tensor_spec.TensorSpec([3], tf.int32),
))
agent = bern_ts_agent.BernoulliThompsonSamplingAgent(
time_step_spec, self._action_spec, batch_size=2
)
observations = (
np.array([1, 1], dtype=np.float32),
np.array([[0, 0, 1], [0, 0, 1]], dtype=np.int32),
)
actions = np.array([0, 1], dtype=np.int32)
rewards = np.array([1.0, 0.0], dtype=np.float32)
initial_step, final_step = _get_initial_and_final_steps_with_action_mask(
observations, rewards
)
action_step = _get_action_step(actions)
experience = _get_experience(initial_step, action_step, final_step)
loss, _ = agent.train(experience, None)
self.evaluate(tf.compat.v1.initialize_all_variables())
self.assertAllClose(self.evaluate(loss), -1.0)
if __name__ == '__main__':
tf.test.main()