def get_agent_by_name(agent_name, time_step_spec, action_spec): if agent_name == 'LinUCB': return lin_ucb_agent.LinearUCBAgent(time_step_spec=time_step_spec, action_spec=action_spec, dtype=tf.float32) elif agent_name == 'LinTS': return lin_ts_agent.LinearThompsonSamplingAgent( time_step_spec=time_step_spec, action_spec=action_spec, dtype=tf.float32) elif agent_name == 'epsGreedy': network = q_network.QNetwork( input_tensor_spec=time_step_spec.observation, action_spec=action_spec, fc_layer_params=(50, 50, 50)) return neural_epsilon_greedy_agent.NeuralEpsilonGreedyAgent( time_step_spec=time_step_spec, action_spec=action_spec, reward_network=network, optimizer=tf.compat.v1.train.AdamOptimizer(learning_rate=0.05), epsilon=0.1) elif agent_name == 'mix': emit_policy_info = ( policy_utilities.InfoFields.PREDICTED_REWARDS_MEAN, ) network = q_network.QNetwork( input_tensor_spec=time_step_spec.observation, action_spec=action_spec, fc_layer_params=(50, 50, 50)) agent_epsgreedy = neural_epsilon_greedy_agent.NeuralEpsilonGreedyAgent( time_step_spec=time_step_spec, action_spec=action_spec, reward_network=network, optimizer=tf.compat.v1.train.AdamOptimizer(learning_rate=0.05), emit_policy_info=emit_policy_info, epsilon=0.1) agent_linucb = lin_ucb_agent.LinearUCBAgent( time_step_spec=time_step_spec, action_spec=action_spec, emit_policy_info=emit_policy_info, dtype=tf.float32) agent_random = neural_epsilon_greedy_agent.NeuralEpsilonGreedyAgent( time_step_spec=time_step_spec, action_spec=action_spec, reward_network=network, optimizer=tf.compat.v1.train.AdamOptimizer(learning_rate=0.05), emit_policy_info=emit_policy_info, epsilon=1.) agent_halfrandom = neural_epsilon_greedy_agent.NeuralEpsilonGreedyAgent( time_step_spec=time_step_spec, action_spec=action_spec, reward_network=network, optimizer=tf.compat.v1.train.AdamOptimizer(learning_rate=0.05), emit_policy_info=emit_policy_info, epsilon=0.5) return exp3_mixture_agent.Exp3MixtureAgent( (agent_epsgreedy, agent_linucb, agent_random, agent_halfrandom))
def testTrainPerArmAgent(self): obs_spec = bandit_spec_utils.create_per_arm_observation_spec(2, 3, 3) time_step_spec = ts.time_step_spec(obs_spec) reward_net = (global_and_arm_feature_network. create_feed_forward_common_tower_network( obs_spec, (4, 3), (3, 4), (4, 2))) optimizer = tf.compat.v1.train.GradientDescentOptimizer( learning_rate=0.1) agent = neural_epsilon_greedy_agent.NeuralEpsilonGreedyAgent( time_step_spec, self._action_spec, reward_network=reward_net, optimizer=optimizer, epsilon=0.1, accepts_per_arm_features=True) observations = { bandit_spec_utils.GLOBAL_FEATURE_KEY: tf.constant([[1, 2], [3, 4]], dtype=tf.float32), bandit_spec_utils.PER_ARM_FEATURE_KEY: tf.cast(tf.reshape(tf.range(18), shape=[2, 3, 3]), dtype=tf.float32) } time_steps = ts.restart(observations, batch_size=2) policy = agent.policy action_step = policy.action(time_steps) self.evaluate(tf.compat.v1.initialize_all_variables()) actions = self.evaluate(action_step.action) self.assertAllEqual(actions.shape, (2, ))
def main(unused_argv): tf.compat.v1.enable_v2_behavior() # The trainer only runs with V2 enabled. data_path = FLAGS.data_path if not data_path: raise ValueError('Please specify the location of the data file.') env = movielens_py_environment.MovieLensPyEnvironment( data_path, RANK_K, BATCH_SIZE, num_movies=20) environment = tf_py_environment.TFPyEnvironment(env) optimal_reward_fn = functools.partial( environment_utilities.compute_optimal_reward_with_movielens_environment, environment=environment) optimal_action_fn = functools.partial( environment_utilities.compute_optimal_action_with_movielens_environment, environment=environment) if FLAGS.agent == 'LinUCB': agent = lin_ucb_agent.LinearUCBAgent( time_step_spec=environment.time_step_spec(), action_spec=environment.action_spec(), tikhonov_weight=0.001, alpha=AGENT_ALPHA, dtype=tf.float32) elif FLAGS.agent == 'LinTS': agent = lin_ts_agent.LinearThompsonSamplingAgent( time_step_spec=environment.time_step_spec(), action_spec=environment.action_spec(), dtype=tf.float32) elif FLAGS.agent == 'epsGreedy': network = q_network.QNetwork( input_tensor_spec=environment.time_step_spec().observation, action_spec=environment.action_spec(), fc_layer_params=LAYERS) agent = eps_greedy_agent.NeuralEpsilonGreedyAgent( time_step_spec=environment.time_step_spec(), action_spec=environment.action_spec(), reward_network=network, optimizer=tf.compat.v1.train.AdamOptimizer(learning_rate=LR), epsilon=EPSILON) elif FLAGS.agent == 'DropoutTS': agent = dropout_ts_agent.DropoutThompsonSamplingAgent( time_step_spec=environment.time_step_spec(), action_spec=environment.action_spec(), dropout_rate=DROPOUT_RATE, network_layers=LAYERS, optimizer=tf.compat.v1.train.AdamOptimizer(learning_rate=LR)) regret_metric = tf_bandit_metrics.RegretMetric(optimal_reward_fn) suboptimal_arms_metric = tf_bandit_metrics.SuboptimalArmsMetric( optimal_action_fn) trainer.train( root_dir=FLAGS.root_dir, agent=agent, environment=environment, training_loops=TRAINING_LOOPS, steps_per_loop=STEPS_PER_LOOP, additional_metrics=[regret_metric, suboptimal_arms_metric])
def testAgentWithDifferentSubagentsUpdate(self): num_actions = 3 context_dim = 2 batch_size = 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) agent1 = lin_ucb_agent.LinearUCBAgent( time_step_spec, action_spec, emit_policy_info=(policy_utilities.InfoFields.PREDICTED_REWARDS_MEAN,)) reward_net = q_network.QNetwork( input_tensor_spec=observation_spec, action_spec=action_spec, fc_layer_params=(4, 3, 2)) agent2 = neural_epsilon_greedy_agent.NeuralEpsilonGreedyAgent( time_step_spec, action_spec, reward_network=reward_net, emit_policy_info=(policy_utilities.InfoFields.PREDICTED_REWARDS_MEAN,), optimizer=tf.compat.v1.train.GradientDescentOptimizer( learning_rate=0.1), epsilon=0.1) agents = [agent1, agent2] dist = tfd.Categorical(probs=tf.Variable([0., 1.])) mixed_agent = WeightRotatingMixtureAgent(dist, 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, 2, num_actions) experience = _get_experience(initial_step, action_step, final_step) self.evaluate(mixed_agent.initialize()) loss_info = mixed_agent.train(experience) self.evaluate(loss_info)
def main(unused_argv): tf.compat.v1.enable_v2_behavior() # The trainer only runs with V2 enabled. class LinearNormalReward(object): def __init__(self, theta): self.theta = theta def __call__(self, x): mu = np.dot(x, self.theta) return np.random.normal(mu, 1) def _global_context_sampling_fn(): return np.random.randint(-10, 10, [4]).astype(np.float32) def _arm_context_sampling_fn(): return np.random.randint(-2, 3, [5]).astype(np.float32) reward_fn = LinearNormalReward(HIDDEN_PARAM) env = sspe.StationaryStochasticPerArmPyEnvironment( _global_context_sampling_fn, _arm_context_sampling_fn, NUM_ACTIONS, reward_fn, batch_size=BATCH_SIZE) environment = tf_py_environment.TFPyEnvironment(env) obs_spec = environment.observation_spec() if FLAGS.network == 'commontower': network = (global_and_arm_feature_network. create_feed_forward_common_tower_network( obs_spec, (4, 3), (3, 4), (4, 2))) elif FLAGS.network == 'dotproduct': network = (global_and_arm_feature_network. create_feed_forward_dot_product_network( obs_spec, (4, 3, 6), (3, 4, 6))) agent = neural_epsilon_greedy_agent.NeuralEpsilonGreedyAgent( time_step_spec=environment.time_step_spec(), action_spec=environment.action_spec(), reward_network=network, optimizer=tf.compat.v1.train.AdamOptimizer(learning_rate=LR), epsilon=EPSILON, accepts_per_arm_features=True, emit_policy_info=policy_utilities.InfoFields.PREDICTED_REWARDS_MEAN) optimal_reward_fn = functools.partial(optimal_reward, hidden_param=HIDDEN_PARAM) optimal_action_fn = functools.partial(optimal_action, hidden_param=HIDDEN_PARAM) regret_metric = tf_bandit_metrics.RegretMetric(optimal_reward_fn) suboptimal_arms_metric = tf_bandit_metrics.SuboptimalArmsMetric( optimal_action_fn) trainer.train(root_dir=FLAGS.root_dir, agent=agent, environment=environment, training_loops=TRAINING_LOOPS, steps_per_loop=STEPS_PER_LOOP, additional_metrics=[regret_metric, suboptimal_arms_metric])
def main(unused_argv): tf.compat.v1.enable_v2_behavior() # The trainer only runs with V2 enabled. with tf.device('/CPU:0'): # due to b/128333994 covertype_dataset = dataset_utilities.convert_covertype_dataset( FLAGS.covertype_csv) covertype_reward_distribution = tfd.Independent( tfd.Deterministic(tf.eye(7)), reinterpreted_batch_ndims=2) environment = ce.ClassificationBanditEnvironment( covertype_dataset, covertype_reward_distribution, BATCH_SIZE) optimal_reward_fn = functools.partial( env_util.compute_optimal_reward_with_classification_environment, environment=environment) optimal_action_fn = functools.partial( env_util.compute_optimal_action_with_classification_environment, environment=environment) if FLAGS.agent == 'LinUCB': agent = lin_ucb_agent.LinearUCBAgent( time_step_spec=environment.time_step_spec(), action_spec=environment.action_spec(), alpha=AGENT_ALPHA, emit_log_probability=False, dtype=tf.float32) elif FLAGS.agent == 'LinTS': agent = lin_ts_agent.LinearThompsonSamplingAgent( time_step_spec=environment.time_step_spec(), action_spec=environment.action_spec(), alpha=AGENT_ALPHA, dtype=tf.float32) elif FLAGS.agent == 'epsGreedy': network = q_network.QNetwork( input_tensor_spec=environment.time_step_spec().observation, action_spec=environment.action_spec(), fc_layer_params=LAYERS) agent = eps_greedy_agent.NeuralEpsilonGreedyAgent( time_step_spec=environment.time_step_spec(), action_spec=environment.action_spec(), reward_network=network, optimizer=tf.compat.v1.train.AdamOptimizer(learning_rate=LR), epsilon=EPSILON) regret_metric = tf_bandit_metrics.RegretMetric(optimal_reward_fn) suboptimal_arms_metric = tf_bandit_metrics.SuboptimalArmsMetric( optimal_action_fn) trainer.train( root_dir=FLAGS.root_dir, agent=agent, environment=environment, training_loops=TRAINING_LOOPS, steps_per_loop=STEPS_PER_LOOP, additional_metrics=[regret_metric, suboptimal_arms_metric])
def main(unused_argv): tf.enable_resource_variables() with tf.device('/CPU:0'): # due to b/128333994 env = wheel_py_environment.WheelPyEnvironment(DELTA, MU_BASE, STD_BASE, MU_HIGH, STD_HIGH, BATCH_SIZE) environment = tf_py_environment.TFPyEnvironment(env) optimal_reward_fn = functools.partial( environment_utilities.tf_wheel_bandit_compute_optimal_reward, delta=DELTA, mu_inside=MU_BASE[0], mu_high=MU_HIGH) optimal_action_fn = functools.partial( environment_utilities.tf_wheel_bandit_compute_optimal_action, delta=DELTA) if FLAGS.agent == 'LinUCB': agent = lin_ucb_agent.LinearUCBAgent( time_step_spec=environment.time_step_spec(), action_spec=environment.action_spec(), alpha=AGENT_ALPHA, dtype=tf.float32) elif FLAGS.agent == 'LinTS': agent = lin_ts_agent.LinearThompsonSamplingAgent( time_step_spec=environment.time_step_spec(), action_spec=environment.action_spec(), alpha=AGENT_ALPHA, dtype=tf.float32) elif FLAGS.agent == 'epsGreedy': network = q_network.QNetwork( input_tensor_spec=environment.time_step_spec().observation, action_spec=environment.action_spec(), fc_layer_params=LAYERS) agent = eps_greedy_agent.NeuralEpsilonGreedyAgent( time_step_spec=environment.time_step_spec(), action_spec=environment.action_spec(), reward_network=network, optimizer=tf.compat.v1.train.AdamOptimizer(learning_rate=LR), epsilon=EPSILON) regret_metric = tf_bandit_metrics.RegretMetric(optimal_reward_fn) suboptimal_arms_metric = tf_bandit_metrics.SuboptimalArmsMetric( optimal_action_fn) trainer.train( root_dir=FLAGS.root_dir, agent=agent, environment=environment, training_loops=TRAINING_LOOPS, steps_per_loop=STEPS_PER_LOOP, additional_metrics=[regret_metric, suboptimal_arms_metric])
def testPolicyWithEpsilonGreedy(self): reward_net = DummyNet(self._observation_spec, self._action_spec) agent = neural_epsilon_greedy_agent.NeuralEpsilonGreedyAgent( self._time_step_spec, self._action_spec, reward_network=reward_net, optimizer=None, epsilon=0.1) observations = tf.constant([[1, 2], [3, 4]], 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.global_variables_initializer()) actions = self.evaluate(action_step.action) self.assertIn(actions[0], [0, 1, 2]) self.assertIn(actions[1], [0, 1, 2])
def testPolicyWithEpsilonGreedyAndActionMask(self): reward_net = DummyNet(self._observation_spec, self._action_spec) obs_spec = (tensor_spec.TensorSpec([2], tf.float32), tensor_spec.TensorSpec([3], tf.int32)) agent = neural_epsilon_greedy_agent.NeuralEpsilonGreedyAgent( ts.time_step_spec(obs_spec), self._action_spec, reward_network=reward_net, optimizer=None, observation_and_action_constraint_splitter=lambda x: (x[0], x[1]), epsilon=0.1) observations = (tf.constant([[1, 2], [3, 4]], dtype=tf.float32), tf.constant([[0, 0, 1], [0, 1, 0]], dtype=tf.int32)) 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.global_variables_initializer()) actions = self.evaluate(action_step.action) self.assertAllEqual(actions, [2, 1])
def main(unused_argv): tf.compat.v1.enable_v2_behavior() # The trainer only runs with V2 enabled. class LinearNormalReward(object): def __init__(self, theta): self.theta = theta def __call__(self, x): mu = np.dot(x, self.theta) return np.random.normal(mu, 1) def _global_context_sampling_fn(): return np.random.randint(-10, 10, [4]).astype(np.float32) def _arm_context_sampling_fn(): return np.random.randint(-2, 3, [5]).astype(np.float32) reward_fn = LinearNormalReward(HIDDEN_PARAM) observation_and_action_constraint_splitter = None num_actions_fn = None variable_action_method = bandit_spec_utils.VariableActionMethod.FIXED if FLAGS.add_num_actions_feature: num_actions_fn = lambda: NUM_ACTIONS variable_action_method = ( bandit_spec_utils.VariableActionMethod.NUM_ACTIONS_FEATURE) env = sspe.StationaryStochasticPerArmPyEnvironment( _global_context_sampling_fn, _arm_context_sampling_fn, NUM_ACTIONS, reward_fn, num_actions_fn, batch_size=BATCH_SIZE, variable_action_method=variable_action_method) environment = tf_py_environment.TFPyEnvironment(env) if FLAGS.agent == 'LinUCB': agent = lin_ucb_agent.LinearUCBAgent( time_step_spec=environment.time_step_spec(), action_spec=environment.action_spec(), alpha=AGENT_ALPHA, accepts_per_arm_features=True, dtype=tf.float32) elif FLAGS.agent == 'LinTS': agent = lin_ts_agent.LinearThompsonSamplingAgent( time_step_spec=environment.time_step_spec(), action_spec=environment.action_spec(), alpha=AGENT_ALPHA, observation_and_action_constraint_splitter=( observation_and_action_constraint_splitter), accepts_per_arm_features=True, dtype=tf.float32) elif FLAGS.agent == 'epsGreedy': obs_spec = environment.observation_spec() if FLAGS.network == 'commontower': network = (global_and_arm_feature_network. create_feed_forward_common_tower_network( obs_spec, (40, 30), (30, 40), (40, 20))) elif FLAGS.network == 'dotproduct': network = (global_and_arm_feature_network. create_feed_forward_dot_product_network( obs_spec, (4, 3, 6), (3, 4, 6))) agent = neural_epsilon_greedy_agent.NeuralEpsilonGreedyAgent( time_step_spec=environment.time_step_spec(), action_spec=environment.action_spec(), reward_network=network, optimizer=tf.compat.v1.train.AdamOptimizer(learning_rate=LR), epsilon=EPSILON, observation_and_action_constraint_splitter=( observation_and_action_constraint_splitter), accepts_per_arm_features=True, emit_policy_info=policy_utilities.InfoFields.PREDICTED_REWARDS_MEAN ) elif FLAGS.agent == 'NeuralLinUCB': obs_spec = environment.observation_spec() network = (global_and_arm_feature_network. create_feed_forward_common_tower_network( obs_spec, (40, 30), (30, 40), (40, 20), ENCODING_DIM)) agent = neural_linucb_agent.NeuralLinUCBAgent( time_step_spec=environment.time_step_spec(), action_spec=environment.action_spec(), encoding_network=network, encoding_network_num_train_steps=EPS_PHASE_STEPS, encoding_dim=ENCODING_DIM, optimizer=tf.compat.v1.train.AdamOptimizer(learning_rate=LR), alpha=1.0, gamma=1.0, epsilon_greedy=EPSILON, accepts_per_arm_features=True, debug_summaries=True, summarize_grads_and_vars=True, emit_policy_info=policy_utilities.InfoFields.PREDICTED_REWARDS_MEAN ) def _all_rewards(observation, hidden_param): """Outputs rewards for all actions, given an observation.""" hidden_param = tf.cast(hidden_param, dtype=tf.float32) global_obs = observation[bandit_spec_utils.GLOBAL_FEATURE_KEY] per_arm_obs = observation[bandit_spec_utils.PER_ARM_FEATURE_KEY] num_actions = tf.shape(per_arm_obs)[1] tiled_global = tf.tile(tf.expand_dims(global_obs, axis=1), [1, num_actions, 1]) concatenated = tf.concat([tiled_global, per_arm_obs], axis=-1) rewards = tf.linalg.matvec(concatenated, hidden_param) return rewards def optimal_reward(observation, hidden_param): return tf.reduce_max(_all_rewards(observation, hidden_param), axis=1) def optimal_action(observation, hidden_param): return tf.argmax(_all_rewards(observation, hidden_param), axis=1, output_type=tf.int32) optimal_reward_fn = functools.partial(optimal_reward, hidden_param=HIDDEN_PARAM) optimal_action_fn = functools.partial(optimal_action, hidden_param=HIDDEN_PARAM) regret_metric = tf_bandit_metrics.RegretMetric(optimal_reward_fn) suboptimal_arms_metric = tf_bandit_metrics.SuboptimalArmsMetric( optimal_action_fn) if FLAGS.drop_arm_obs: drop_arm_feature_fn = functools.partial( bandit_spec_utils.drop_arm_observation) else: drop_arm_feature_fn = None trainer.train(root_dir=FLAGS.root_dir, agent=agent, environment=environment, training_loops=TRAINING_LOOPS, steps_per_loop=STEPS_PER_LOOP, additional_metrics=[regret_metric, suboptimal_arms_metric], training_data_spec_transformation_fn=drop_arm_feature_fn)
def get_agent_by_name(agent_name, time_step_spec, action_spec): """Helper function that outputs an agent. Args: agent_name: The name (string) of the desired agent. time_step_spec: The time step spec of the environment on which the agent acts. action_spec: The action spec on which the agent acts. Returns: The desired agent. """ accepts_per_arm_features = isinstance( time_step_spec.observation, dict) and 'per_arm' in time_step_spec.observation if agent_name == 'LinUCB': return lin_ucb_agent.LinearUCBAgent( time_step_spec=time_step_spec, action_spec=action_spec, dtype=tf.float32, accepts_per_arm_features=accepts_per_arm_features) elif agent_name == 'LinTS': return lin_ts_agent.LinearThompsonSamplingAgent( time_step_spec=time_step_spec, action_spec=action_spec, dtype=tf.float32, accepts_per_arm_features=accepts_per_arm_features) elif agent_name == 'epsGreedy': if accepts_per_arm_features: network = (global_and_arm_feature_network. create_feed_forward_common_tower_network( time_step_spec.observation, (20, 20), (20, 20), (20, 20))) else: network = q_network.QNetwork( input_tensor_spec=time_step_spec.observation, action_spec=action_spec, fc_layer_params=(50, 50, 50)) return neural_epsilon_greedy_agent.NeuralEpsilonGreedyAgent( time_step_spec=time_step_spec, action_spec=action_spec, reward_network=network, optimizer=tf.compat.v1.train.AdamOptimizer(learning_rate=0.05), epsilon=0.1, accepts_per_arm_features=accepts_per_arm_features) elif agent_name == 'mix': assert not accepts_per_arm_features, 'Per-arm mixture agent not supported.' emit_policy_info = ( policy_utilities.InfoFields.PREDICTED_REWARDS_MEAN, ) network = q_network.QNetwork( input_tensor_spec=time_step_spec.observation, action_spec=action_spec, fc_layer_params=(50, 50, 50)) agent_epsgreedy = neural_epsilon_greedy_agent.NeuralEpsilonGreedyAgent( time_step_spec=time_step_spec, action_spec=action_spec, reward_network=network, optimizer=tf.compat.v1.train.AdamOptimizer(learning_rate=0.05), emit_policy_info=emit_policy_info, epsilon=0.1) agent_linucb = lin_ucb_agent.LinearUCBAgent( time_step_spec=time_step_spec, action_spec=action_spec, emit_policy_info=emit_policy_info, dtype=tf.float32) agent_random = neural_epsilon_greedy_agent.NeuralEpsilonGreedyAgent( time_step_spec=time_step_spec, action_spec=action_spec, reward_network=network, optimizer=tf.compat.v1.train.AdamOptimizer(learning_rate=0.05), emit_policy_info=emit_policy_info, epsilon=1.) agent_halfrandom = neural_epsilon_greedy_agent.NeuralEpsilonGreedyAgent( time_step_spec=time_step_spec, action_spec=action_spec, reward_network=network, optimizer=tf.compat.v1.train.AdamOptimizer(learning_rate=0.05), emit_policy_info=emit_policy_info, epsilon=0.5) return exp3_mixture_agent.Exp3MixtureAgent( (agent_epsgreedy, agent_linucb, agent_random, agent_halfrandom))
def main(unused_argv): tf.compat.v1.enable_v2_behavior() # The trainer only runs with V2 enabled. data_path = FLAGS.data_path if not data_path: raise ValueError('Please specify the location of the data file.') if FLAGS.per_arm: env = movielens_per_arm_py_environment.MovieLensPerArmPyEnvironment( data_path, RANK_K, BATCH_SIZE, num_actions=NUM_ACTIONS, csv_delimiter='\t') else: env = movielens_py_environment.MovieLensPyEnvironment( data_path, RANK_K, BATCH_SIZE, num_movies=NUM_ACTIONS, csv_delimiter='\t') environment = tf_py_environment.TFPyEnvironment(env) optimal_reward_fn = functools.partial( environment_utilities.compute_optimal_reward_with_movielens_environment, environment=environment) optimal_action_fn = functools.partial( environment_utilities.compute_optimal_action_with_movielens_environment, environment=environment) if FLAGS.agent == 'LinUCB': agent = lin_ucb_agent.LinearUCBAgent( time_step_spec=environment.time_step_spec(), action_spec=environment.action_spec(), tikhonov_weight=0.001, alpha=AGENT_ALPHA, dtype=tf.float32, accepts_per_arm_features=FLAGS.per_arm) elif FLAGS.agent == 'LinTS': agent = lin_ts_agent.LinearThompsonSamplingAgent( time_step_spec=environment.time_step_spec(), action_spec=environment.action_spec(), dtype=tf.float32, accepts_per_arm_features=FLAGS.per_arm) elif FLAGS.agent == 'epsGreedy': if FLAGS.per_arm: network = ( global_and_arm_feature_network .create_feed_forward_dot_product_network( environment.time_step_spec().observation, global_layers=LAYERS, arm_layers=LAYERS)) else: network = q_network.QNetwork( input_tensor_spec=environment.time_step_spec().observation, action_spec=environment.action_spec(), fc_layer_params=LAYERS) agent = eps_greedy_agent.NeuralEpsilonGreedyAgent( time_step_spec=environment.time_step_spec(), action_spec=environment.action_spec(), reward_network=network, optimizer=tf.compat.v1.train.AdamOptimizer(learning_rate=LR), epsilon=EPSILON, emit_policy_info='predicted_rewards_mean', info_fields_to_inherit_from_greedy=['predicted_rewards_mean']) elif FLAGS.agent == 'DropoutTS': train_step_counter = tf.compat.v1.train.get_or_create_global_step() def dropout_fn(): return tf.math.maximum( tf.math.reciprocal_no_nan(1.01 + tf.cast(train_step_counter, tf.float32)), 0.0003) agent = dropout_ts_agent.DropoutThompsonSamplingAgent( time_step_spec=environment.time_step_spec(), action_spec=environment.action_spec(), dropout_rate=dropout_fn, network_layers=LAYERS, optimizer=tf.compat.v1.train.AdamOptimizer(learning_rate=LR)) regret_metric = tf_bandit_metrics.RegretMetric(optimal_reward_fn) suboptimal_arms_metric = tf_bandit_metrics.SuboptimalArmsMetric( optimal_action_fn) trainer.train( root_dir=FLAGS.root_dir, agent=agent, environment=environment, training_loops=TRAINING_LOOPS, steps_per_loop=STEPS_PER_LOOP, additional_metrics=[regret_metric, suboptimal_arms_metric])
def main(unused_argv): tf.compat.v1.enable_v2_behavior() # The trainer only runs with V2 enabled. with tf.device('/CPU:0'): # due to b/128333994 if FLAGS.normalize_reward_fns: action_reward_fns = (environment_utilities. normalized_sliding_linear_reward_fn_generator( CONTEXT_DIM, NUM_ACTIONS, REWARD_NOISE_VARIANCE)) else: action_reward_fns = ( environment_utilities.sliding_linear_reward_fn_generator( CONTEXT_DIM, NUM_ACTIONS, REWARD_NOISE_VARIANCE)) env = sspe.StationaryStochasticPyEnvironment(functools.partial( environment_utilities.context_sampling_fn, batch_size=BATCH_SIZE, context_dim=CONTEXT_DIM), action_reward_fns, batch_size=BATCH_SIZE) mask_split_fn = None if FLAGS.num_disabled_actions > 0: mask_split_fn = lambda x: (x[0], x[1]) env = wrappers.ExtraDisabledActionsWrapper( env, FLAGS.num_disabled_actions) environment = tf_py_environment.TFPyEnvironment(env) optimal_reward_fn = functools.partial( environment_utilities.tf_compute_optimal_reward, per_action_reward_fns=action_reward_fns) optimal_action_fn = functools.partial( environment_utilities.tf_compute_optimal_action, per_action_reward_fns=action_reward_fns) network_input_spec = environment.time_step_spec().observation if FLAGS.num_disabled_actions > 0: def _apply_only_to_observation(fn): def result_fn(obs): return fn(obs[0]) return result_fn optimal_action_fn = _apply_only_to_observation(optimal_action_fn) optimal_reward_fn = _apply_only_to_observation(optimal_reward_fn) network_input_spec = network_input_spec[0] network = q_network.QNetwork(input_tensor_spec=network_input_spec, action_spec=environment.action_spec(), fc_layer_params=LAYERS) if FLAGS.agent == 'LinUCB': agent = lin_ucb_agent.LinearUCBAgent( time_step_spec=environment.time_step_spec(), action_spec=environment.action_spec(), alpha=AGENT_ALPHA, dtype=tf.float32, observation_and_action_constraint_splitter=mask_split_fn) elif FLAGS.agent == 'LinTS': agent = lin_ts_agent.LinearThompsonSamplingAgent( time_step_spec=environment.time_step_spec(), action_spec=environment.action_spec(), alpha=AGENT_ALPHA, dtype=tf.float32, observation_and_action_constraint_splitter=mask_split_fn) elif FLAGS.agent == 'epsGreedy': agent = neural_epsilon_greedy_agent.NeuralEpsilonGreedyAgent( time_step_spec=environment.time_step_spec(), action_spec=environment.action_spec(), reward_network=network, optimizer=tf.compat.v1.train.AdamOptimizer(learning_rate=LR), epsilon=EPSILON, observation_and_action_constraint_splitter=mask_split_fn) elif FLAGS.agent == 'Boltzmann': train_step_counter = tf.compat.v1.train.get_or_create_global_step() boundaries = [500] temp_values = [1000.0, TEMPERATURE] temp_schedule = tf.keras.optimizers.schedules.PiecewiseConstantDecay( boundaries, temp_values) def _temperature_fn(): # Any variable used in the function needs to be saved in the policy. # This is true by default for the `train_step_counter`. return temp_schedule(train_step_counter) agent = neural_boltzmann_agent.NeuralBoltzmannAgent( time_step_spec=environment.time_step_spec(), action_spec=environment.action_spec(), reward_network=network, temperature=_temperature_fn, optimizer=tf.compat.v1.train.AdamOptimizer(learning_rate=LR), observation_and_action_constraint_splitter=mask_split_fn, train_step_counter=train_step_counter) # This is needed, otherwise the PolicySaver complains. agent.policy.step = train_step_counter elif FLAGS.agent == 'BoltzmannGumbel': num_samples_list = [ tf.compat.v2.Variable(0, dtype=tf.int32, name='num_samples_{}'.format(k)) for k in range(NUM_ACTIONS) ] agent = neural_boltzmann_agent.NeuralBoltzmannAgent( time_step_spec=environment.time_step_spec(), action_spec=environment.action_spec(), reward_network=network, boltzmann_gumbel_exploration_constant=250.0, optimizer=tf.compat.v1.train.AdamOptimizer(learning_rate=LR), observation_and_action_constraint_splitter=mask_split_fn, num_samples_list=num_samples_list) elif FLAGS.agent == 'Mix': assert FLAGS.num_disabled_actions == 0, ( 'Extra actions with mixture agent not supported.') emit_policy_info = policy_utilities.InfoFields.PREDICTED_REWARDS_MEAN agent_linucb = lin_ucb_agent.LinearUCBAgent( time_step_spec=environment.time_step_spec(), action_spec=environment.action_spec(), emit_policy_info=emit_policy_info, alpha=AGENT_ALPHA, dtype=tf.float32) agent_lints = lin_ts_agent.LinearThompsonSamplingAgent( time_step_spec=environment.time_step_spec(), action_spec=environment.action_spec(), emit_policy_info=emit_policy_info, alpha=AGENT_ALPHA, dtype=tf.float32) agent_epsgreedy = neural_epsilon_greedy_agent.NeuralEpsilonGreedyAgent( time_step_spec=environment.time_step_spec(), action_spec=environment.action_spec(), reward_network=network, optimizer=tf.compat.v1.train.AdamOptimizer(learning_rate=LR), emit_policy_info=emit_policy_info, epsilon=EPSILON) agent = exp3_mixture_agent.Exp3MixtureAgent( (agent_linucb, agent_lints, agent_epsgreedy)) regret_metric = tf_bandit_metrics.RegretMetric(optimal_reward_fn) suboptimal_arms_metric = tf_bandit_metrics.SuboptimalArmsMetric( optimal_action_fn) trainer.train( root_dir=FLAGS.root_dir, agent=agent, environment=environment, training_loops=TRAINING_LOOPS, steps_per_loop=STEPS_PER_LOOP, additional_metrics=[regret_metric, suboptimal_arms_metric])
def main(unused_argv): tf.compat.v1.enable_v2_behavior() # The trainer only runs with V2 enabled. with tf.device('/CPU:0'): # due to b/128333994 action_reward_fns = ( environment_utilities.structured_linear_reward_fn_generator( CONTEXT_DIM, NUM_ACTIONS, REWARD_NOISE_VARIANCE)) env = sspe.StationaryStochasticPyEnvironment(functools.partial( environment_utilities.context_sampling_fn, batch_size=BATCH_SIZE, context_dim=CONTEXT_DIM), action_reward_fns, batch_size=BATCH_SIZE) environment = tf_py_environment.TFPyEnvironment(env) optimal_reward_fn = functools.partial( environment_utilities.tf_compute_optimal_reward, per_action_reward_fns=action_reward_fns) optimal_action_fn = functools.partial( environment_utilities.tf_compute_optimal_action, per_action_reward_fns=action_reward_fns) if FLAGS.agent == 'LinUCB': agent = lin_ucb_agent.LinearUCBAgent( time_step_spec=environment.time_step_spec(), action_spec=environment.action_spec(), alpha=AGENT_ALPHA, dtype=tf.float32) elif FLAGS.agent == 'epsGreedy': laplacian_matrix = utils.build_laplacian_over_ordinal_integer_actions( environment.action_spec()) network = q_network.QNetwork( input_tensor_spec=environment.time_step_spec().observation, action_spec=environment.action_spec(), fc_layer_params=REWARD_NETWORK_LAYER_PARAMS) agent = eps_greedy_agent.NeuralEpsilonGreedyAgent( time_step_spec=environment.time_step_spec(), action_spec=environment.action_spec(), reward_network=network, optimizer=tf.compat.v1.train.AdamOptimizer( learning_rate=NN_LEARNING_RATE), epsilon=EPSILON, laplacian_matrix=laplacian_matrix, laplacian_smoothing_weight=0.01) elif FLAGS.agent == 'LinTS': agent = lin_ts_agent.LinearThompsonSamplingAgent( time_step_spec=environment.time_step_spec(), action_spec=environment.action_spec(), alpha=AGENT_ALPHA, dtype=tf.float32) regret_metric = tf_bandit_metrics.RegretMetric(optimal_reward_fn) suboptimal_arms_metric = tf_bandit_metrics.SuboptimalArmsMetric( optimal_action_fn) trainer.train( root_dir=FLAGS.root_dir, agent=agent, environment=environment, training_loops=TRAINING_LOOPS, steps_per_loop=STEPS_PER_LOOP, additional_metrics=[regret_metric, suboptimal_arms_metric])
def main(unused_argv): tf.compat.v1.enable_v2_behavior() # The trainer only runs with V2 enabled. with tf.device('/CPU:0'): # due to b/128333994 if FLAGS.normalize_reward_fns: action_reward_fns = ( environment_utilities.normalized_sliding_linear_reward_fn_generator( CONTEXT_DIM, NUM_ACTIONS, REWARD_NOISE_VARIANCE)) else: action_reward_fns = ( environment_utilities.sliding_linear_reward_fn_generator( CONTEXT_DIM, NUM_ACTIONS, REWARD_NOISE_VARIANCE)) env = sspe.StationaryStochasticPyEnvironment( functools.partial( environment_utilities.context_sampling_fn, batch_size=BATCH_SIZE, context_dim=CONTEXT_DIM), action_reward_fns, batch_size=BATCH_SIZE) mask_split_fn = None if FLAGS.num_disabled_actions > 0: mask_split_fn = lambda x: (x[0], x[1]) env = wrappers.ExtraDisabledActionsWrapper(env, FLAGS.num_disabled_actions) environment = tf_py_environment.TFPyEnvironment(env) optimal_reward_fn = functools.partial( environment_utilities.tf_compute_optimal_reward, per_action_reward_fns=action_reward_fns) optimal_action_fn = functools.partial( environment_utilities.tf_compute_optimal_action, per_action_reward_fns=action_reward_fns) network_input_spec = environment.time_step_spec().observation if FLAGS.num_disabled_actions > 0: def _apply_only_to_observation(fn): def result_fn(obs): return fn(obs[0]) return result_fn optimal_action_fn = _apply_only_to_observation(optimal_action_fn) optimal_reward_fn = _apply_only_to_observation(optimal_reward_fn) network_input_spec = network_input_spec[0] network = q_network.QNetwork( input_tensor_spec=network_input_spec, action_spec=environment.action_spec(), fc_layer_params=LAYERS) if FLAGS.agent == 'LinUCB': agent = lin_ucb_agent.LinearUCBAgent( time_step_spec=environment.time_step_spec(), action_spec=environment.action_spec(), alpha=AGENT_ALPHA, dtype=tf.float32, observation_and_action_constraint_splitter=mask_split_fn) elif FLAGS.agent == 'LinTS': agent = lin_ts_agent.LinearThompsonSamplingAgent( time_step_spec=environment.time_step_spec(), action_spec=environment.action_spec(), alpha=AGENT_ALPHA, dtype=tf.float32, observation_and_action_constraint_splitter=mask_split_fn) elif FLAGS.agent == 'epsGreedy': agent = neural_epsilon_greedy_agent.NeuralEpsilonGreedyAgent( time_step_spec=environment.time_step_spec(), action_spec=environment.action_spec(), reward_network=network, optimizer=tf.compat.v1.train.AdamOptimizer(learning_rate=LR), epsilon=EPSILON, observation_and_action_constraint_splitter=mask_split_fn) elif FLAGS.agent == 'Mix': assert FLAGS.num_disabled_actions == 0, ( 'Extra actions with mixture agent not supported.') emit_policy_info = policy_utilities.InfoFields.PREDICTED_REWARDS_MEAN agent_linucb = lin_ucb_agent.LinearUCBAgent( time_step_spec=environment.time_step_spec(), action_spec=environment.action_spec(), emit_policy_info=emit_policy_info, alpha=AGENT_ALPHA, dtype=tf.float32) agent_lints = lin_ts_agent.LinearThompsonSamplingAgent( time_step_spec=environment.time_step_spec(), action_spec=environment.action_spec(), emit_policy_info=emit_policy_info, alpha=AGENT_ALPHA, dtype=tf.float32) agent_epsgreedy = neural_epsilon_greedy_agent.NeuralEpsilonGreedyAgent( time_step_spec=environment.time_step_spec(), action_spec=environment.action_spec(), reward_network=network, optimizer=tf.compat.v1.train.AdamOptimizer(learning_rate=LR), emit_policy_info=emit_policy_info, epsilon=EPSILON) agent = exp3_mixture_agent.Exp3MixtureAgent( (agent_linucb, agent_lints, agent_epsgreedy)) regret_metric = tf_bandit_metrics.RegretMetric(optimal_reward_fn) suboptimal_arms_metric = tf_bandit_metrics.SuboptimalArmsMetric( optimal_action_fn) trainer.train( root_dir=FLAGS.root_dir, agent=agent, environment=environment, training_loops=TRAINING_LOOPS, steps_per_loop=STEPS_PER_LOOP, additional_metrics=[regret_metric, suboptimal_arms_metric])
def main(unused_argv): tf.compat.v1.enable_v2_behavior() # The trainer only runs with V2 enabled. with tf.device('/CPU:0'): # due to b/128333994 env = wheel_py_environment.WheelPyEnvironment(DELTA, MU_BASE, STD_BASE, MU_HIGH, STD_HIGH, BATCH_SIZE) environment = tf_py_environment.TFPyEnvironment(env) optimal_reward_fn = functools.partial( environment_utilities.tf_wheel_bandit_compute_optimal_reward, delta=DELTA, mu_inside=MU_BASE[0], mu_high=MU_HIGH) optimal_action_fn = functools.partial( environment_utilities.tf_wheel_bandit_compute_optimal_action, delta=DELTA) network = q_network.QNetwork( input_tensor_spec=environment.time_step_spec().observation, action_spec=environment.action_spec(), fc_layer_params=(LAYERS)) if FLAGS.agent == 'LinUCB': agent = lin_ucb_agent.LinearUCBAgent( time_step_spec=environment.time_step_spec(), action_spec=environment.action_spec(), alpha=AGENT_ALPHA, dtype=tf.float32) elif FLAGS.agent == 'LinTS': agent = lin_ts_agent.LinearThompsonSamplingAgent( time_step_spec=environment.time_step_spec(), action_spec=environment.action_spec(), alpha=AGENT_ALPHA, dtype=tf.float32) elif FLAGS.agent == 'epsGreedy': agent = eps_greedy_agent.NeuralEpsilonGreedyAgent( time_step_spec=environment.time_step_spec(), action_spec=environment.action_spec(), reward_network=network, optimizer=tf.compat.v1.train.AdamOptimizer(learning_rate=LR), epsilon=EPSILON) elif FLAGS.agent == 'random': agent = eps_greedy_agent.NeuralEpsilonGreedyAgent( time_step_spec=environment.time_step_spec(), action_spec=environment.action_spec(), reward_network=network, optimizer=tf.compat.v1.train.AdamOptimizer(learning_rate=LR), epsilon=1.) elif FLAGS.agent == 'Mix': emit_policy_info = ( policy_utilities.InfoFields.PREDICTED_REWARDS_MEAN, ) agent_epsgreedy = eps_greedy_agent.NeuralEpsilonGreedyAgent( time_step_spec=environment.time_step_spec(), action_spec=environment.action_spec(), reward_network=network, optimizer=tf.compat.v1.train.AdamOptimizer(learning_rate=LR), emit_policy_info=emit_policy_info, epsilon=EPSILON) agent_linucb = lin_ucb_agent.LinearUCBAgent( time_step_spec=environment.time_step_spec(), action_spec=environment.action_spec(), alpha=AGENT_ALPHA, emit_policy_info=emit_policy_info, dtype=tf.float32) agent_random = eps_greedy_agent.NeuralEpsilonGreedyAgent( time_step_spec=environment.time_step_spec(), action_spec=environment.action_spec(), reward_network=network, optimizer=tf.compat.v1.train.AdamOptimizer(learning_rate=LR), emit_policy_info=emit_policy_info, epsilon=1.) agent_halfrandom = eps_greedy_agent.NeuralEpsilonGreedyAgent( time_step_spec=environment.time_step_spec(), action_spec=environment.action_spec(), reward_network=network, optimizer=tf.compat.v1.train.AdamOptimizer(learning_rate=LR), emit_policy_info=emit_policy_info, epsilon=0.5) agent = exp3_mixture_agent.Exp3MixtureAgent( (agent_epsgreedy, agent_linucb, agent_random, agent_halfrandom)) regret_metric = tf_bandit_metrics.RegretMetric(optimal_reward_fn) suboptimal_arms_metric = tf_bandit_metrics.SuboptimalArmsMetric( optimal_action_fn) trainer.train( root_dir=FLAGS.root_dir, agent=agent, environment=environment, training_loops=TRAINING_LOOPS, steps_per_loop=STEPS_PER_LOOP, additional_metrics=[regret_metric, suboptimal_arms_metric])
def main(unused_argv): tf.compat.v1.enable_v2_behavior() # The trainer only runs with V2 enabled. feature_dict = np.array([str(i) for i in range(DICTIONARY_SIZE)]) def _global_context_sampling_fn(): """Generates one sample of global features. It generates a dictionary of size `NUM_GLOBAL_FEATURES`, with the following syntax: {..., 'global_feature_4': ['43'], ... } That is, the values are one-element numpy arrays of strings. Returns: A dictionary with string keys and numpy string array values. """ generated_features = feature_dict[np.random.randint( 0, DICTIONARY_SIZE, [NUM_GLOBAL_FEATURES])] global_features = { 'global_feature_{}'.format(i): generated_features[[i]] for i in range(NUM_GLOBAL_FEATURES) } return global_features def _arm_context_sampling_fn(): """Generates one sample of arm features. It generates a dictionary of size `NUM_ARM_FEATURES`, with the following syntax: {..., 'arm_feature_7': ['29'], ... } That is, the values are one-element numpy arrays of strings. Note that the output sample is for one arm and one non-batched time step. Returns: A dictionary with string keys and numpy string array values. """ generated_features = feature_dict[np.random.randint( 0, DICTIONARY_SIZE, [NUM_ARM_FEATURES])] arm_features = { 'arm_feature_{}'.format(i): generated_features[[i]] for i in range(NUM_ARM_FEATURES) } return arm_features def _reward_fn(global_features, arm_features): """Outputs a [0, 1] float given a sample. The output reward is generated by hashing the concatenation of feature keys and values, then adding all up, taking modulo by 1000, and normalizing. Args: global_features: A dictionary with string keys and 1d string numpy array values. arm_features: A dictionary with string keys and 1d string numpy array values. Returns: A float value between 0 and 1. """ hashed_global = 0 for x, y in global_features.items(): hashed_global += hash(x + y[0]) hashed_arm = 0 for x, y in arm_features.items(): hashed_arm += hash(x + y[0]) return (hashed_global + hashed_arm) % 1000 / 1000 env = sspe.StationaryStochasticStructuredPyEnvironment( _global_context_sampling_fn, _arm_context_sampling_fn, NUM_ACTIONS, _reward_fn, batch_size=BATCH_SIZE) environment = tf_py_environment.TFPyEnvironment(env) def make_string_feature(name): return tf.feature_column.indicator_column( tf.feature_column.categorical_column_with_vocabulary_list( name, feature_dict)) global_columns = [ make_string_feature('global_feature_{}'.format(i)) for i in range(NUM_GLOBAL_FEATURES) ] arm_columns = [ make_string_feature('arm_feature_{}'.format(i)) for i in range(NUM_ARM_FEATURES) ] obs_spec = environment.observation_spec() if FLAGS.agent == 'epsGredy': network = (global_and_arm_feature_network. create_feed_forward_common_tower_network( obs_spec, (4, 3), (3, 4), (4, 2), global_preprocessing_combiner=tf.compat.v2.keras.layers. DenseFeatures(global_columns), arm_preprocessing_combiner=tf.compat.v2.keras.layers. DenseFeatures(arm_columns))) agent = neural_epsilon_greedy_agent.NeuralEpsilonGreedyAgent( time_step_spec=environment.time_step_spec(), action_spec=environment.action_spec(), reward_network=network, optimizer=tf.compat.v1.train.AdamOptimizer(learning_rate=LR), epsilon=EPSILON, accepts_per_arm_features=True, emit_policy_info=policy_utilities.InfoFields.PREDICTED_REWARDS_MEAN ) elif FLAGS.agent == 'NeuralLinUCB': network = (global_and_arm_feature_network. create_feed_forward_common_tower_network( obs_spec, (40, 30), (30, 40), (40, 20), ENCODING_DIM, global_preprocessing_combiner=tf.compat.v2.keras.layers. DenseFeatures(global_columns), arm_preprocessing_combiner=tf.compat.v2.keras.layers. DenseFeatures(arm_columns))) agent = neural_linucb_agent.NeuralLinUCBAgent( time_step_spec=environment.time_step_spec(), action_spec=environment.action_spec(), encoding_network=network, encoding_network_num_train_steps=EPS_PHASE_STEPS, encoding_dim=ENCODING_DIM, optimizer=tf.compat.v1.train.AdamOptimizer(learning_rate=LR), alpha=1.0, gamma=1.0, epsilon_greedy=EPSILON, accepts_per_arm_features=True, debug_summaries=True, summarize_grads_and_vars=True, emit_policy_info=policy_utilities.InfoFields.PREDICTED_REWARDS_MEAN ) if FLAGS.drop_arm_obs: drop_arm_feature_fn = bandit_spec_utils.drop_arm_observation else: drop_arm_feature_fn = None trainer.train(root_dir=FLAGS.root_dir, agent=agent, environment=environment, training_loops=TRAINING_LOOPS, steps_per_loop=STEPS_PER_LOOP, training_data_spec_transformation_fn=drop_arm_feature_fn)