Evolutionary Function Approximation for Reinforcement Learning

  title={Evolutionary Function Approximation for Reinforcement Learning},
  author={Shimon Whiteson and Peter Stone},
  journal={Journal of Machine Learning Research},
Temporal difference methods are theoretically grounded and empirically effective methods for addressing sequential decision making problems with delayed rewards. Most problems of real-world interest require coupling TD methods with a function approximator to represent the value function. However, using function approximators requires manually making crucial representational decisions. This paper introduces evolutionary function approximation, a novel approach to automatically selecting function… CONTINUE READING
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