A Reinforcement Learning Scheme for a Partially-Observable Multi-Agent Game

Shin Ishii, Hajime Fujita, Masaoki Mitsutake, Tatsuya Yamazaki, Jun Matsuda, Yoichiro Matsuno
<span title="">2005</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/h4nnd7sxwzcwhetu5qkjbcdh6u" style="color: black;">Machine Learning</a> </i> &nbsp;
We formulate an automatic strategy acquisition problem for the multi-agent card game "Hearts" as a reinforcement learning problem. The problem can approximately be dealt with in the framework of a partially observable Markov decision process (POMDP) for a single-agent system. Hearts is an example of imperfect information games, which are more difficult to deal with than perfect information games. A POMDP is a decision problem that includes a process for estimating unobservable state variables.
more &raquo; ... y regarding missing information as unobservable state variables, an imperfect information game can be formulated as a POMDP. However, the game of Hearts is a realistic problem that has a huge number of possible states, even when it is approximated as a singleagent system. Therefore, further approximation is necessary to make the strategy acquisition problem tractable. This article presents an approximation method based on estimating unobservable state variables and predicting the actions of the other agents. Simulation results show that our reinforcement learning method is applicable to such a difficult multi-agent problem.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s10994-005-0461-8">doi:10.1007/s10994-005-0461-8</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rp7oo5rj3nf55exvfqjnt5nb5u">fatcat:rp7oo5rj3nf55exvfqjnt5nb5u</a> </span>
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