Evolutionary Algorithms For Reinforcement Learning

# Evolutionary Algorithms for Reinforcement Learning

**arXiv ID:** 1106.0221
**Authors:** J. J. Grefenstette, D. E. Moriarty, A. C. Schultz
**Published:** 2011-06-01T16:16:14Z
**Abstract:**
There are two distinct approaches to solving reinforcement    learning problems, namely, searching in value function space and    searching in policy space.  Temporal difference methods and    evolutionary algorithms are well-known examples of these approaches.    Kaelbling, Littman and Moore recently provided an informative survey    of temporal difference methods.  This article focuses on the    application of evolutionary algorithms to the reinforcement learning    problem, emphasizing alternative policy representations, credit    assignment methods, and problem-specific genetic operators.  Strengths    and weaknesses of the evolutionary approach to reinforcement learning    are presented, along with a survey of representative applications.

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## References

- [arXiv:1106.0221](http://arxiv.org/abs/1106.0221v1)