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exp3_mixture_agent_test.py
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exp3_mixture_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_mixture_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
import tensorflow_probability as tfp
from tf_agents.bandits.agents import exp3_mixture_agent
from tf_agents.bandits.agents import lin_ucb_agent
from tf_agents.bandits.drivers import driver_utils
from tf_agents.bandits.policies import mixture_policy
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
from tf_agents.utils import test_utils
tfd = tfp.distributions
def _get_initial_and_final_steps(batch_size, context_dim):
observation = np.array(range(batch_size * context_dim)).reshape(
[batch_size, context_dim]
)
initial_step = time_step.TimeStep(
tf.constant(
time_step.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'),
tf.constant(
observation,
dtype=tf.float32,
shape=[batch_size, context_dim],
name='observation',
),
)
final_step = time_step.TimeStep(
tf.constant(
time_step.StepType.LAST,
dtype=tf.int32,
shape=[batch_size],
name='step_type',
),
tf.constant(0.5, dtype=tf.float32, shape=[batch_size], name='reward'),
tf.constant(1.0, dtype=tf.float32, shape=[batch_size], name='discount'),
tf.constant(
observation + 100.0,
dtype=tf.float32,
shape=[batch_size, context_dim],
name='observation',
),
)
return initial_step, final_step
def _get_action_step(action, num_agents, num_actions, emit_policy_info):
batch_size = tf.shape(action)[0]
choices = tf.constant(num_agents - 1, shape=action.shape, dtype=tf.int32)
predicted_rewards_mean = (
tf.zeros([batch_size, num_actions]) if emit_policy_info else ()
)
return policy_step.PolicyStep(
action=tf.convert_to_tensor(action),
info={
mixture_policy.MIXTURE_AGENT_ID: choices,
mixture_policy.SUBPOLICY_INFO: policy_utilities.PolicyInfo(
predicted_rewards_mean=predicted_rewards_mean
),
},
)
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
)
def test_cases():
return parameterized.named_parameters(
{
'testcase_name': '_batch1_contextdim10_numagents2_info',
'batch_size': 1,
'context_dim': 10,
'num_agents': 2,
'emit_policy_info': True,
},
{
'testcase_name': '_batch3_contextdim7_numagents17_noinfo',
'batch_size': 3,
'context_dim': 7,
'num_agents': 17,
'emit_policy_info': False,
},
{
'testcase_name': '_batch4_contextdim5_numagents10_info',
'batch_size': 4,
'context_dim': 5,
'num_agents': 10,
'emit_policy_info': True,
},
)
class Exp3MixtureAgentTest(test_utils.TestCase, parameterized.TestCase):
def setUp(self):
super(Exp3MixtureAgentTest, self).setUp()
tf.compat.v1.enable_resource_variables()
@test_cases()
def testInitializeAgent(
self, batch_size, context_dim, num_agents, emit_policy_info
):
num_actions = 7
observation_spec = tensor_spec.TensorSpec([context_dim], tf.float32)
time_step_spec = time_step.time_step_spec(observation_spec)
action_spec = tensor_spec.BoundedTensorSpec(
dtype=tf.int32, shape=(), minimum=0, maximum=num_actions - 1
)
agents = [
lin_ucb_agent.LinearUCBAgent(time_step_spec, action_spec)
for _ in range(num_agents)
]
mixed_agent = exp3_mixture_agent.Exp3MixtureAgent(agents)
self.evaluate(mixed_agent.initialize())
@test_cases()
def testMixtureUpdate(
self, batch_size, context_dim, num_agents, emit_policy_info
):
num_actions = 5
observation_spec = tensor_spec.TensorSpec([context_dim], tf.float32)
time_step_spec = time_step.time_step_spec(observation_spec)
action_spec = tensor_spec.BoundedTensorSpec(
dtype=tf.int32, shape=(), minimum=0, maximum=num_actions - 1
)
agents = []
policy_info = (
(policy_utilities.InfoFields.PREDICTED_REWARDS_MEAN,)
if emit_policy_info
else ()
)
for _ in range(num_agents):
agents.append(
lin_ucb_agent.LinearUCBAgent(
time_step_spec, action_spec, emit_policy_info=policy_info
)
)
mixed_agent = exp3_mixture_agent.Exp3MixtureAgent(agents)
initial_step, final_step = _get_initial_and_final_steps(
batch_size, context_dim
)
action = np.random.randint(num_actions, size=batch_size, dtype=np.int32)
action_step = _get_action_step(
action, num_agents, num_actions, emit_policy_info
)
if emit_policy_info:
self.assertEqual(
self.evaluate(
action_step.info['subpolicy_info'].predicted_rewards_mean[0, 0]
),
0,
)
experience = _get_experience(initial_step, action_step, final_step)
self.evaluate(mixed_agent.initialize())
self.evaluate(mixed_agent._variable_collection.reward_aggregates)
self.evaluate(mixed_agent.train(experience))
reward_aggregates = self.evaluate(
mixed_agent._variable_collection.reward_aggregates
)
self.assertAllInSet(reward_aggregates[: num_agents - 1], [0.999])
agent_prob = 1 / num_agents
est_rewards = 0.5 / agent_prob
per_step_update = est_rewards
last_agent_update = 1 - batch_size * per_step_update
self.assertAllClose(reward_aggregates[-1], last_agent_update * 0.999)
if __name__ == '__main__':
tf.test.main()