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Batch RL (#238)

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Gal Leibovich
2019-03-19 18:07:09 +02:00
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rl_coach/off_policy_evaluators/ope_manager.py Normal file
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#
# Copyright (c) 2019 Intel Corporation
#
# 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 collections import namedtuple
import numpy as np
from typing import List
from rl_coach.architectures.architecture import Architecture
from rl_coach.core_types import Episode, Batch
from rl_coach.off_policy_evaluators.bandits.doubly_robust import DoublyRobust
from rl_coach.off_policy_evaluators.rl.sequential_doubly_robust import SequentialDoublyRobust
from rl_coach.core_types import Transition
OpeSharedStats = namedtuple("OpeSharedStats", ['all_reward_model_rewards', 'all_policy_probs',
'all_v_values_reward_model_based', 'all_rewards', 'all_actions',
'all_old_policy_probs', 'new_policy_prob', 'rho_all_dataset'])
OpeEstimation = namedtuple("OpeEstimation", ['ips', 'dm', 'dr', 'seq_dr'])
class OpeManager(object):
def __init__(self):
self.doubly_robust = DoublyRobust()
self.sequential_doubly_robust = SequentialDoublyRobust()
@staticmethod
def _prepare_ope_shared_stats(dataset_as_transitions: List[Transition], batch_size: int,
reward_model: Architecture, q_network: Architecture,
network_keys: List) -> OpeSharedStats:
"""
Do the preparations needed for different estimators.
Some of the calcuations are shared, so we centralize all the work here.
:param dataset_as_transitions: The evaluation dataset in the form of transitions.
:param batch_size: The batch size to use.
:param reward_model: A reward model to be used by DR
:param q_network: The Q network whose its policy we evaluate.
:param network_keys: The network keys used for feeding the neural networks.
:return:
"""
# IPS
all_reward_model_rewards, all_policy_probs, all_old_policy_probs = [], [], []
all_v_values_reward_model_based, all_v_values_q_model_based, all_rewards, all_actions = [], [], [], []
for i in range(int(len(dataset_as_transitions) / batch_size) + 1):
batch = dataset_as_transitions[i * batch_size: (i + 1) * batch_size]
batch_for_inference = Batch(batch)
all_reward_model_rewards.append(reward_model.predict(
batch_for_inference.states(network_keys)))
# TODO can we get rid of the 'output_heads[0]', and have some way of a cleaner API?
q_values, sm_values = q_network.predict(batch_for_inference.states(network_keys),
outputs=[q_network.output_heads[0].output,
q_network.output_heads[0].softmax])
# TODO why is this needed?
q_values = q_values[0]
all_policy_probs.append(sm_values)
all_v_values_reward_model_based.append(np.sum(all_policy_probs[-1] * all_reward_model_rewards[-1], axis=1))
all_v_values_q_model_based.append(np.sum(all_policy_probs[-1] * q_values, axis=1))
all_rewards.append(batch_for_inference.rewards())
all_actions.append(batch_for_inference.actions())
all_old_policy_probs.append(batch_for_inference.info('all_action_probabilities')
[range(len(batch_for_inference.actions())), batch_for_inference.actions()])
for j, t in enumerate(batch):
t.update_info({
'q_value': q_values[j],
'softmax_policy_prob': all_policy_probs[-1][j],
'v_value_q_model_based': all_v_values_q_model_based[-1][j],
})
all_reward_model_rewards = np.concatenate(all_reward_model_rewards, axis=0)
all_policy_probs = np.concatenate(all_policy_probs, axis=0)
all_v_values_reward_model_based = np.concatenate(all_v_values_reward_model_based, axis=0)
all_rewards = np.concatenate(all_rewards, axis=0)
all_actions = np.concatenate(all_actions, axis=0)
all_old_policy_probs = np.concatenate(all_old_policy_probs, axis=0)
# generate model probabilities
new_policy_prob = all_policy_probs[np.arange(all_actions.shape[0]), all_actions]
rho_all_dataset = new_policy_prob / all_old_policy_probs
return OpeSharedStats(all_reward_model_rewards, all_policy_probs, all_v_values_reward_model_based,
all_rewards, all_actions, all_old_policy_probs, new_policy_prob, rho_all_dataset)
def evaluate(self, dataset_as_episodes: List[Episode], batch_size: int, discount_factor: float,
reward_model: Architecture, q_network: Architecture, network_keys: List) -> OpeEstimation:
"""
Run all the OPEs and get estimations of the current policy performance based on the evaluation dataset.
:param dataset_as_episodes: The evaluation dataset.
:param batch_size: Batch size to use for the estimators.
:param discount_factor: The standard RL discount factor.
:param reward_model: A reward model to be used by DR
:param q_network: The Q network whose its policy we evaluate.
:param network_keys: The network keys used for feeding the neural networks.
:return: An OpeEstimation tuple which groups together all the OPE estimations
"""
# TODO this seems kind of slow, review performance
dataset_as_transitions = [t for e in dataset_as_episodes for t in e.transitions]
ope_shared_stats = self._prepare_ope_shared_stats(dataset_as_transitions, batch_size, reward_model,
q_network, network_keys)
ips, dm, dr = self.doubly_robust.evaluate(ope_shared_stats)
seq_dr = self.sequential_doubly_robust.evaluate(dataset_as_episodes, discount_factor)
return OpeEstimation(ips, dm, dr, seq_dr)