Asynchronous Methods for Deep Reinforcement Learning
Volodymyr Mnih
1
VMNIH@GOOGLE.COM
Adrià Puigdomènech Badia
1
ADRIAP@GOOGLE.COM
Mehdi Mirza
1,2
MIRZAMOM@IRO.UMONTREAL.CA
Alex Graves
1
GRAVESA@GOOGLE.COM
Tim Harley
1
THARLEY@GOOGLE.COM
Timothy P. Lillicrap
1
COUNTZERO@GOOGLE.COM
David Silver
1
DAVIDSILVER@GOOGLE.COM
Koray Kavukcuoglu
1
KORAYK@GOOGLE.COM
1
Google DeepMind
2
Montreal Institute for Learning Algorithms (MILA), University of Montreal
Abstract
We propose a conceptually simple and
lightweight framework for deep reinforce-
ment learning that uses asynchronous gradient
descent for optimization of deep neural network
controllers. We present asynchronous variants of
four standard reinforcement learning algorithms
and show that parallel actor-learners have a
stabilizing effect on training allowing all four
methods to successfully train neural network
controllers. The best performing method, an
asynchronous variant of actor-critic, surpasses
the current state-of-the-art on the Atari domain
while training for half the time on a single
multi-core CPU instead of a GPU. Furthermore,
we show that asynchronous actor-critic succeeds
on a wide variety of continuous motor control
problems as well as on a new task of navigating
random 3D mazes using a visual input.
1. Introduction
Deep neural networks provide rich representations that can
enable reinforcement learning (RL) algorithms to perform
effectively. However, it was previously thought that the
combination of simple online RL algorithms with deep
neural networks was fundamentally unstable. Instead, a va-
riety of solutions have been proposed to stabilize the algo-
rithm (Riedmiller, 2005; Mnih et al., 2013; 2015; Van Has-
selt et al., 2015; Schulman et al., 2015a). These approaches
share a common idea: the sequence of observed data en-
countered by an online RL agent is non-stationary, and on-
Proceedings of the 33
rd
International Conference on Machine
Learning, New York, NY, USA, 2016. JMLR: W&CP volume
48. Copyright 2016 by the author(s).
line RL updates are strongly correlated. By storing the
agent’s data in an experience replay memory, the data can
be batched (Riedmiller, 2005; Schulman et al., 2015a) or
randomly sampled (Mnih et al., 2013; 2015; Van Hasselt
et al., 2015) from different time-steps. Aggregating over
memory in this way reduces non-stationarity and decorre-
lates updates, but at the same time limits the methods to
off-policy reinforcement learning algorithms.
Deep RL algorithms based on experience replay have
achieved unprecedented success in challenging domains
such as Atari 2600. However, experience replay has several
drawbacks: it uses more memory and computation per real
interaction; and it requires off-policy learning algorithms
that can update from data generated by an older policy.
In this paper we provide a very different paradigm for deep
reinforcement learning. Instead of experience replay, we
asynchronously execute multiple agents in parallel, on mul-
tiple instances of the environment. This parallelism also
decorrelates the agents’ data into a more stationary process,
since at any given time-step the parallel agents will be ex-
periencing a variety of different states. This simple idea
enables a much larger spectrum of fundamental on-policy
RL algorithms, such as Sarsa, n-step methods, and actor-
critic methods, as well as off-policy RL algorithms such
as Q-learning, to be applied robustly and effectively using
deep neural networks.
Our parallel reinforcement learning paradigm also offers
practical benefits. Whereas previous approaches to deep re-
inforcement learning rely heavily on specialized hardware
such as GPUs (Mnih et al., 2015; Van Hasselt et al., 2015;
Schaul et al., 2015) or massively distributed architectures
(Nair et al., 2015), our experiments run on a single machine
with a standard multi-core CPU. When applied to a vari-
ety of Atari 2600 domains, on many games asynchronous
reinforcement learning achieves better results, in far less
arXiv:1602.01783v2 [cs.LG] 16 Jun 2016
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