Efficient Reinforcement Learning through Symbiotic Evolution
作者:David E. Moriarty, Risto Mikkulainen
摘要
This article presents a new reinforcement learning method called SANE (Symbiotic, Adaptive Neuro-Evolution), which evolves a population of neurons through genetic algorithms to form a neural network capable of performing a task. Symbiotic evolution promotes both cooperation and specialization, which results in a fast, efficient genetic search and discourages convergence to suboptimal solutions. In the inverted pendulum problem, SANE formed effective networks 9 to 16 times faster than the Adaptive Heuristic Critic and 2 times faster than Q-learning and the GENITOR neuro-evolution approach without loss of generalization. Such efficient learning, combined with few domain assumptions, make SANE a promising approach to a broad range of reinforcement learning problems, including many real-world applications.
论文关键词:Neuro-Evolution, Reinforcement Learning, Genetic Algorithms, Neural Networks
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论文官网地址:https://doi.org/10.1023/A:1018004120707