|
|
Game Theory and Multi-agent Reinforcement Learning Host Publication: Reinforcement Learning: State-of-the-Art Authors: A. Nowé, P. Vrancx and Y. De Hauwere Publisher: Springer Verlag Publication Year: 2012 Number of Pages: 30 ISBN: 978-3-642-27644-6
Abstract: Reinforcement Learning was originally developed for Markov Decision
Processes (MDPs). It allows a single agent to learn a policy that maximizes a
possibly delayed reward signal in a stochastic stationary environment. It
guarantees convergence to the optimal policy, provided that the agent can
sufficiently experiment and the environment in which it is operating is
Markovian. However, when multiple agents apply reinforcement learning in a shared
environment, this might be beyond the MDP model. In such systems, the optimal
policy of an agent depends not only on the environment, but on the policies of
the other agents as well. These situations arise naturally in a variety of
domains, such as: robotics, telecommunications, economics, distributed control,
auctions, traffic light control, etc. In these domains multi-agent learning is
used, either because of the complexity of the domain or because control is
inherently decentralized. In such systems it is important that agents are capable
of discovering good solutions to the problem at hand either by coordinating with
other learners or by competing with them. This chapter focuses on the application
reinforcement learning techniques in multi-agent systems. We describe a basic
learning framework based on the economic research into game theory, and
illustrate the additional complexity that arises in such systems. We also
described a representative selection of algorithms for the different areas
of multi-agent reinforcement learning research
|
|
