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BCQ variant on top of DDQN (#276)

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* kNN based model for predicting which actions to drop
* fix for seeds with batch rl
This commit is contained in:
Gal Leibovich
2019-04-16 17:06:23 +03:00
committed by GitHub
parent bdb9b224a8
commit 4741b0b916
11 changed files with 451 additions and 62 deletions
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36
rl_coach/agents/ddqn_agent.py
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@@ -17,9 +17,7 @@
from typing import Union
import numpy as np
from rl_coach.agents.dqn_agent import DQNAgentParameters
from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent
from rl_coach.agents.dqn_agent import DQNAgent, DQNAgentParameters
from rl_coach.core_types import EnvironmentSteps
from rl_coach.schedules import LinearSchedule
@@ -37,36 +35,10 @@ class DDQNAgentParameters(DQNAgentParameters):
# Double DQN - https://arxiv.org/abs/1509.06461
class DDQNAgent(ValueOptimizationAgent):
class DDQNAgent(DQNAgent):
def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
super().__init__(agent_parameters, parent)
def learn_from_batch(self, batch):
network_keys = self.ap.network_wrappers['main'].input_embedders_parameters.keys()
def select_actions(self, next_states, q_st_plus_1):
return np.argmax(self.networks['main'].online_network.predict(next_states), 1)
selected_actions = np.argmax(self.networks['main'].online_network.predict(batch.next_states(network_keys)), 1)
q_st_plus_1, TD_targets = self.networks['main'].parallel_prediction([
(self.networks['main'].target_network, batch.next_states(network_keys)),
(self.networks['main'].online_network, batch.states(network_keys))
])
# add Q value samples for logging
self.q_values.add_sample(TD_targets)
# initialize with the current prediction so that we will
# only update the action that we have actually done in this transition
TD_errors = []
for i in range(batch.size):
new_target = batch.rewards()[i] + \
(1.0 - batch.game_overs()[i]) * self.ap.algorithm.discount * q_st_plus_1[i][selected_actions[i]]
TD_errors.append(np.abs(new_target - TD_targets[i, batch.actions()[i]]))
TD_targets[i, batch.actions()[i]] = new_target
# update errors in prioritized replay buffer
importance_weights = self.update_transition_priorities_and_get_weights(TD_errors, batch)
result = self.networks['main'].train_and_sync_networks(batch.states(network_keys), TD_targets,
importance_weights=importance_weights)
total_loss, losses, unclipped_grads = result[:3]
return total_loss, losses, unclipped_grads