A Neural Reinforcement Learning System
نویسندگان
چکیده
In this paper we present a reinforcement learning (RL) system based on neural circuits. The neural RL system is benchmarked against a Monte Carlo (MC) RL algorithm on two tasks. The first task is the classical n-armed bandit problem and the second task is path finding in a maze. The neural RL system performs equally well or better than the MC RL algorithm. The RL system presented is very flexible and general; it can easily be incorporated with other neural based systems, e.g. attractor memories.
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تاریخ انتشار 2002