Learning to coordinate without sharing information
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
TD-Gammon, a self-teaching backgammon program, achieves master-level play
Neural Computation
Reinforcement learning of non-Markov decision processes
Artificial Intelligence - Special volume on computational research on interaction and agency, part 2
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Elevator Group Control Using Multiple Reinforcement Learning Agents
Machine Learning
Learning Team Strategies: Soccer Case Studies
Machine Learning
A multi-agent reinforcement learning method for a partially-observable competitive game
Proceedings of the fifth international conference on Autonomous agents
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Control of exploitation-exploration meta-parameter in reinforcement learning
Neural Networks - Computational models of neuromodulation
Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Memory Approaches to Reinforcement Learning in Non-Markovian Domains
Memory Approaches to Reinforcement Learning in Non-Markovian Domains
Multi-Agent Reinforcement Learning: An Approach Based on the Other Agent's Internal Model
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
Reinforcement learning with selective perception and hidden state
Reinforcement learning with selective perception and hidden state
Large-scale dynamic optimization using teams of reinforcement learning agents
Large-scale dynamic optimization using teams of reinforcement learning agents
On-line EM Algorithm for the Normalized Gaussian Network
Neural Computation
GIB: imperfect information in a computationally challenging game
Journal of Artificial Intelligence Research
Strategy-acquisition system for video trading card game
ACE '08 Proceedings of the 2008 International Conference on Advances in Computer Entertainment Technology
Feature extraction for decision-theoretic planning in partially observable environments
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
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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. By 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 single-agent 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.