A reinforcement learning scheme for a multi-agent card game
نویسندگان
چکیده
We formulate an automatic strategy acquisition problem for the multi-agent card game “Hearts” as a reinforcement learning (RL) problem. Since there are often a lot of unobservable cards in this game, RL is approximately dealt with in the framework of a partially observable Markov decision process (POMDP). This article presents a POMDP-RL method based on estimation of unobservable state variables and prediction of actions of the opponent agents. Simulation results show our model-based POMDP-RL method is applicable to a realistic multi-agent problem.
منابع مشابه
A reinforcement learning scheme for a multi-agent card game with Monte Carlo state estimation
This article presents the state estimation method based on Monte Carlo sampling in a partially observable situation. We formulate an automatic strategy acquisition problem for the multi-agent card game “Hearts” as a reinforcement learning (RL) problem. Since there are often a lot of unobservable cards in this game, RL is dealt with in the framework of a partially observable Markov decision proc...
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