17,070 research outputs found
On Reinforcement Learning for Full-length Game of StarCraft
StarCraft II poses a grand challenge for reinforcement learning. The main
difficulties of it include huge state and action space and a long-time horizon.
In this paper, we investigate a hierarchical reinforcement learning approach
for StarCraft II. The hierarchy involves two levels of abstraction. One is the
macro-action automatically extracted from expert's trajectories, which reduces
the action space in an order of magnitude yet remains effective. The other is a
two-layer hierarchical architecture which is modular and easy to scale,
enabling a curriculum transferring from simpler tasks to more complex tasks.
The reinforcement training algorithm for this architecture is also
investigated. On a 64x64 map and using restrictive units, we achieve a winning
rate of more than 99\% against the difficulty level-1 built-in AI. Through the
curriculum transfer learning algorithm and a mixture of combat model, we can
achieve over 93\% winning rate of Protoss against the most difficult
non-cheating built-in AI (level-7) of Terran, training within two days using a
single machine with only 48 CPU cores and 8 K40 GPUs. It also shows strong
generalization performance, when tested against never seen opponents including
cheating levels built-in AI and all levels of Zerg and Protoss built-in AI. We
hope this study could shed some light on the future research of large-scale
reinforcement learning.Comment: Appeared in AAAI 201
Multiscale Markov Decision Problems: Compression, Solution, and Transfer Learning
Many problems in sequential decision making and stochastic control often have
natural multiscale structure: sub-tasks are assembled together to accomplish
complex goals. Systematically inferring and leveraging hierarchical structure,
particularly beyond a single level of abstraction, has remained a longstanding
challenge. We describe a fast multiscale procedure for repeatedly compressing,
or homogenizing, Markov decision processes (MDPs), wherein a hierarchy of
sub-problems at different scales is automatically determined. Coarsened MDPs
are themselves independent, deterministic MDPs, and may be solved using
existing algorithms. The multiscale representation delivered by this procedure
decouples sub-tasks from each other and can lead to substantial improvements in
convergence rates both locally within sub-problems and globally across
sub-problems, yielding significant computational savings. A second fundamental
aspect of this work is that these multiscale decompositions yield new transfer
opportunities across different problems, where solutions of sub-tasks at
different levels of the hierarchy may be amenable to transfer to new problems.
Localized transfer of policies and potential operators at arbitrary scales is
emphasized. Finally, we demonstrate compression and transfer in a collection of
illustrative domains, including examples involving discrete and continuous
statespaces.Comment: 86 pages, 15 figure
Learning Generalized Reactive Policies using Deep Neural Networks
We present a new approach to learning for planning, where knowledge acquired
while solving a given set of planning problems is used to plan faster in
related, but new problem instances. We show that a deep neural network can be
used to learn and represent a \emph{generalized reactive policy} (GRP) that
maps a problem instance and a state to an action, and that the learned GRPs
efficiently solve large classes of challenging problem instances. In contrast
to prior efforts in this direction, our approach significantly reduces the
dependence of learning on handcrafted domain knowledge or feature selection.
Instead, the GRP is trained from scratch using a set of successful execution
traces. We show that our approach can also be used to automatically learn a
heuristic function that can be used in directed search algorithms. We evaluate
our approach using an extensive suite of experiments on two challenging
planning problem domains and show that our approach facilitates learning
complex decision making policies and powerful heuristic functions with minimal
human input. Videos of our results are available at goo.gl/Hpy4e3
Deep learning for video game playing
In this article, we review recent Deep Learning advances in the context of
how they have been applied to play different types of video games such as
first-person shooters, arcade games, and real-time strategy games. We analyze
the unique requirements that different game genres pose to a deep learning
system and highlight important open challenges in the context of applying these
machine learning methods to video games, such as general game playing, dealing
with extremely large decision spaces and sparse rewards
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