Eecient Reinforcement Learning through Symbiotic Evolution Editor: Leslie Pack Kaelbling
نویسنده
چکیده
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, e cient genetic search and discourages convergence to suboptimal solutions. In the inverted pendulum problem, SANE formed e ective networks 9 to 16 times faster than the Adaptive Heuristic Critic and 2 times faster than Qlearning and the GENITOR neuro-evolutionapproachwithout loss of generalization. Such e cient learning, combined with few domain assumptions, make SANE a promising approach to a broad range of reinforcement learning problems, including many real-world applications.
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