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ddqn_bcq_agent.py
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ddqn_bcq_agent.py
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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 OrderedDict
from copy import deepcopy
from typing import Union, List, Dict
import numpy as np
from rl_coach.agents.dqn_agent import DQNAgentParameters, DQNAlgorithmParameters, DQNAgent
from rl_coach.base_parameters import Parameters
from rl_coach.core_types import EnvironmentSteps, Batch, StateType
from rl_coach.graph_managers.batch_rl_graph_manager import BatchRLGraphManager
from rl_coach.logger import screen
from rl_coach.memories.non_episodic.differentiable_neural_dictionary import AnnoyDictionary
from rl_coach.schedules import LinearSchedule
class NNImitationModelParameters(Parameters):
"""
A parameters module grouping together parameters related to a neural network based action selection.
"""
def __init__(self):
super().__init__()
self.imitation_model_num_epochs = 100
self.mask_out_actions_threshold = 0.35
class KNNParameters(Parameters):
"""
A parameters module grouping together parameters related to a k-Nearest Neighbor based action selection.
"""
def __init__(self):
super().__init__()
self.average_dist_coefficient = 1
self.knn_size = 50000
self.use_state_embedding_instead_of_state = True # useful when the state is too big to be used for kNN
class DDQNBCQAlgorithmParameters(DQNAlgorithmParameters):
"""
:param action_drop_method_parameters: (Parameters)
Defines the mode and related parameters according to which low confidence actions will be filtered out
:param num_steps_between_copying_online_weights_to_target (StepMethod)
Defines the number of steps between every phase of copying online network's weights to the target network's weights
"""
def __init__(self):
super().__init__()
self.action_drop_method_parameters = KNNParameters()
self.num_steps_between_copying_online_weights_to_target = EnvironmentSteps(30000)
class DDQNBCQAgentParameters(DQNAgentParameters):
def __init__(self):
super().__init__()
self.algorithm = DDQNBCQAlgorithmParameters()
self.exploration.epsilon_schedule = LinearSchedule(1, 0.01, 1000000)
self.exploration.evaluation_epsilon = 0.001
@property
def path(self):
return 'rl_coach.agents.ddqn_bcq_agent:DDQNBCQAgent'
# Double DQN - https://arxiv.org/abs/1509.06461
# (a variant on) BCQ - https://arxiv.org/pdf/1812.02900v2.pdf
class DDQNBCQAgent(DQNAgent):
def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
super().__init__(agent_parameters, parent)
if isinstance(self.ap.algorithm.action_drop_method_parameters, KNNParameters):
self.knn_trees = [] # will be filled out later, as we don't have the action space size yet
self.average_dist = 0
def to_embedding(states: Union[List[StateType], Dict]):
if isinstance(states, list):
states = self.prepare_batch_for_inference(states, 'reward_model')
if self.ap.algorithm.action_drop_method_parameters.use_state_embedding_instead_of_state:
return self.networks['reward_model'].online_network.predict(
states,
outputs=[self.networks['reward_model'].online_network.state_embedding[0]])
else:
return states['observation']
self.embedding = to_embedding
elif isinstance(self.ap.algorithm.action_drop_method_parameters, NNImitationModelParameters):
if 'imitation_model' not in self.ap.network_wrappers:
# user hasn't defined params for the reward model. we will use the same params as used for the 'main'
# network.
self.ap.network_wrappers['imitation_model'] = deepcopy(self.ap.network_wrappers['reward_model'])
else:
raise ValueError('Unsupported action drop method {} for DDQNBCQAgent'.format(
type(self.ap.algorithm.action_drop_method_parameters)))
def select_actions(self, next_states, q_st_plus_1):
if isinstance(self.ap.algorithm.action_drop_method_parameters, KNNParameters):
familiarity = np.array([[distance[0] for distance in
knn_tree._get_k_nearest_neighbors_indices(self.embedding(next_states), 1)[0]]
for knn_tree in self.knn_trees]).transpose()
actions_to_mask_out = familiarity > self.ap.algorithm.action_drop_method_parameters.average_dist_coefficient \
* self.average_dist
elif isinstance(self.ap.algorithm.action_drop_method_parameters, NNImitationModelParameters):
familiarity = self.networks['imitation_model'].online_network.predict(next_states)
actions_to_mask_out = familiarity < \
self.ap.algorithm.action_drop_method_parameters.mask_out_actions_threshold
else:
raise ValueError('Unsupported action drop method {} for DDQNBCQAgent'.format(
type(self.ap.algorithm.action_drop_method_parameters)))
masked_next_q_values = self.networks['main'].online_network.predict(next_states)
masked_next_q_values[actions_to_mask_out] = -np.inf
# occassionaly there are states in the batch for which our model shows no confidence for either of the actions
# in that case, we will just randomly assign q_values to actions, since otherwise argmax will always return
# the first action
zero_confidence_rows = (masked_next_q_values.max(axis=1) == -np.inf)
masked_next_q_values[zero_confidence_rows] = np.random.rand(np.sum(zero_confidence_rows),
masked_next_q_values.shape[1])
return np.argmax(masked_next_q_values, 1)
def improve_reward_model(self, epochs: int):
"""
Train both a reward model to be used by the doubly-robust estimator, and some model to be used for BCQ
:param epochs: The total number of epochs to use for training a reward model
:return: None
"""
# we'll be assuming that these gets drawn from the reward model parameters
batch_size = self.ap.network_wrappers['reward_model'].batch_size
network_keys = self.ap.network_wrappers['reward_model'].input_embedders_parameters.keys()
# if using a NN to decide which actions to drop, we'll train the NN here
if isinstance(self.ap.algorithm.action_drop_method_parameters, NNImitationModelParameters):
total_epochs = max(epochs, self.ap.algorithm.action_drop_method_parameters.imitation_model_num_epochs)
else:
total_epochs = epochs
for epoch in range(total_epochs):
# this is fitted from the training dataset
reward_model_loss = 0
imitation_model_loss = 0
total_transitions_processed = 0
for i, batch in enumerate(self.call_memory('get_shuffled_training_data_generator', batch_size)):
batch = Batch(batch)
# reward model
if epoch < epochs:
reward_model_loss += self.get_reward_model_loss(batch)
# imitation model
if isinstance(self.ap.algorithm.action_drop_method_parameters, NNImitationModelParameters) and \
epoch < self.ap.algorithm.action_drop_method_parameters.imitation_model_num_epochs:
target_actions = np.zeros((batch.size, len(self.spaces.action.actions)))
target_actions[range(batch.size), batch.actions()] = 1
imitation_model_loss += self.networks['imitation_model'].train_and_sync_networks(
batch.states(network_keys), target_actions)[0]
total_transitions_processed += batch.size
log = OrderedDict()
log['Epoch'] = epoch
if reward_model_loss:
log['Reward Model Loss'] = reward_model_loss / total_transitions_processed
if imitation_model_loss:
log['Imitation Model Loss'] = imitation_model_loss / total_transitions_processed
screen.log_dict(log, prefix='Training Batch RL Models')
# if using a kNN based model, we'll initialize and build it here.
# initialization cannot be moved to the constructor as we don't have the agent's spaces initialized yet.
if isinstance(self.ap.algorithm.action_drop_method_parameters, KNNParameters):
knn_size = self.ap.algorithm.action_drop_method_parameters.knn_size
if self.ap.algorithm.action_drop_method_parameters.use_state_embedding_instead_of_state:
self.knn_trees = [AnnoyDictionary(
dict_size=knn_size,
key_width=int(self.networks['reward_model'].online_network.state_embedding[0].shape[-1]),
batch_size=knn_size)
for _ in range(len(self.spaces.action.actions))]
else:
self.knn_trees = [AnnoyDictionary(
dict_size=knn_size,
key_width=self.spaces.state['observation'].shape[0],
batch_size=knn_size)
for _ in range(len(self.spaces.action.actions))]
for i, knn_tree in enumerate(self.knn_trees):
state_embeddings = self.embedding([transition.state for transition in self.memory.transitions
if transition.action == i])
knn_tree.add(
keys=state_embeddings,
values=np.expand_dims(np.zeros(state_embeddings.shape[0]), axis=1))
for knn_tree in self.knn_trees:
knn_tree._rebuild_index()
self.average_dist = [[dist[0] for dist in knn_tree._get_k_nearest_neighbors_indices(
keys=self.embedding([transition.state for transition in self.memory.transitions]),
k=1)[0]] for knn_tree in self.knn_trees]
self.average_dist = sum([x for l in self.average_dist for x in l]) # flatten and sum
self.average_dist /= len(self.memory.transitions)
def set_session(self, sess) -> None:
super().set_session(sess)
# we check here if we are in batch-rl, since this is the only place where we have a graph_manager to question
assert isinstance(self.parent_level_manager.parent_graph_manager, BatchRLGraphManager),\
'DDQNBCQ agent can only be used in BatchRL'