Temporal difference learning and TD-Gammon
Communications of the ACM
Multiagent learning using a variable learning rate
Artificial Intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Value-function reinforcement learning in Markov games
Cognitive Systems Research
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Multiagent reinforcement learning problems are especially difficult because of their dynamism and the size of joint state space. In this paper a new benchmark problem is proposed, which involves the need for cooperation, competition and synchronization between agents. The notion of state attractor is introduced, such that agents compute their actions based on the proximity of their current state to the nearest state attractor. A genetic algorithm is used to find the state attractors. This representation can be used as a compact way to define individual or joint policies.