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exp3_agent_test.py
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exp3_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 tf_agents.bandits.agents.exp3_agent."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl.testing import parameterized
import numpy as np
import tensorflow as tf # pylint: disable=g-explicit-tensorflow-version-import
import tensorflow_probability as tfp
from tf_agents.bandits.agents import exp3_agent
from tf_agents.bandits.drivers import driver_utils
from tf_agents.specs import tensor_spec
from tf_agents.trajectories import policy_step
from tf_agents.trajectories import time_step
tfd = tfp.distributions
def _get_initial_and_final_steps(observation_shape, reward):
batch_size = reward.shape[0]
time_step_spec = time_step.time_step_spec(
tensor_spec.TensorSpec(observation_shape, tf.float32)
)
initial_step = tensor_spec.sample_spec_nest(
time_step_spec, outer_dims=(batch_size,)
)
final_step = initial_step._replace(reward=tf.convert_to_tensor(reward))
return initial_step, final_step
def _get_action_step(action, log_prob):
step = policy_step.PolicyStep(action=tf.convert_to_tensor(action))
return step._replace(
info=policy_step.set_log_probability(
step.info, tf.convert_to_tensor(log_prob)
)
)
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 Exp3AgentTest(tf.test.TestCase, parameterized.TestCase):
def setUp(self):
super(Exp3AgentTest, self).setUp()
tf.compat.v1.enable_resource_variables()
@parameterized.named_parameters(
dict(
testcase_name='_trivial',
observation_shape=[],
num_actions=1,
learning_rate=1.0,
),
dict(
testcase_name='_2_2_obs',
observation_shape=[2, 2],
num_actions=5,
learning_rate=0.3,
),
)
def testInitializeAgent(self, observation_shape, num_actions, learning_rate):
time_step_spec = time_step.time_step_spec(
tensor_spec.TensorSpec(observation_shape, tf.float32)
)
action_spec = tensor_spec.BoundedTensorSpec(
dtype=tf.int32, shape=(), minimum=0, maximum=num_actions - 1
)
agent = exp3_agent.Exp3Agent(
time_step_spec=time_step_spec,
action_spec=action_spec,
learning_rate=learning_rate,
)
self.evaluate(agent.initialize())
@parameterized.parameters(
dict(
values=np.array([0.0]),
partitions=np.array([0]),
num_partitions=1,
expected_output=np.array([0.0]),
),
dict(
values=np.array([0, 1, 2, 3, 4, 5]),
partitions=np.array([0, 1, 1, 1, 0, 0]),
num_partitions=2,
expected_output=np.array([9, 6]),
),
)
def testSelectiveSum(
self, values, partitions, num_partitions, expected_output
):
actual_output = exp3_agent.selective_sum(values, partitions, num_partitions)
self.assertAllCloseAccordingToType(expected_output, actual_output)
@parameterized.parameters(
dict(shape=[], seed=1234),
dict(shape=[100], seed=2345),
dict(shape=[3, 4, 3, 6, 2], seed=3456),
)
def testExp3UpdateValueShape(self, shape, seed):
tf.compat.v1.set_random_seed(seed)
reward = tfd.Uniform(0.0, 1.0).sample(shape)
log_prob = tfd.Normal(0.0, 1.0).sample(shape)
update_value = exp3_agent.exp3_update_value(reward, log_prob)
self.assertAllEqual(shape, update_value.shape)
@parameterized.named_parameters(
dict(
testcase_name='_trivial',
observation_shape=[],
num_actions=1,
action=np.array([0], dtype=np.int32),
log_prob=np.array([0.0], dtype=np.float32),
reward=np.array([0.0], dtype=np.float32),
learning_rate=1.0,
),
dict(
testcase_name='_8_rewards',
observation_shape=[2, 2],
num_actions=5,
action=np.array([0, 1, 1, 2, 4, 3, 4, 3], dtype=np.int32),
log_prob=np.log(
[0.1, 0.2, 0.2, 0.4, 0.6, 0.2, 0.4, 0.2], dtype=np.float32
),
reward=np.array(
[0.0, 0.4, 0.3, 0.4, 0.9, 0.4, 0.5, 0.3], dtype=np.float32
),
learning_rate=0.3,
),
)
def testExp3Update(
self,
observation_shape,
num_actions,
action,
log_prob,
reward,
learning_rate,
):
"""Check EXP3 updates for specified actions and rewards."""
# Compute expected update for each action.
expected_update_value = exp3_agent.exp3_update_value(reward, log_prob)
expected_update = np.zeros(num_actions)
for a, u in zip(action, self.evaluate(expected_update_value)):
expected_update[a] += u
# Construct a `Trajectory` for the given action, log prob and reward.
time_step_spec = time_step.time_step_spec(
tensor_spec.TensorSpec(observation_shape, tf.float32)
)
action_spec = tensor_spec.BoundedTensorSpec(
dtype=tf.int32, shape=(), minimum=0, maximum=num_actions - 1
)
initial_step, final_step = _get_initial_and_final_steps(
observation_shape, reward
)
action_step = _get_action_step(action, log_prob)
experience = _get_experience(initial_step, action_step, final_step)
# Construct an agent and perform the update. Record initial and final
# weights.
agent = exp3_agent.Exp3Agent(
time_step_spec=time_step_spec,
action_spec=action_spec,
learning_rate=learning_rate,
)
self.evaluate(agent.initialize())
initial_weights = self.evaluate(agent.weights)
loss_info = agent.train(experience)
self.evaluate(loss_info)
final_weights = self.evaluate(agent.weights)
update = final_weights - initial_weights
# Check that the actual update matches expectations.
self.assertAllClose(expected_update, update)
if __name__ == '__main__':
tf.test.main()