Co-evolution of Shaping Rewards and Meta-Parameters in Reinforcement Learning

  title={Co-evolution of Shaping Rewards and Meta-Parameters in Reinforcement Learning},
  author={Stefan Elfwing and Eiji Uchibe and Kenji Doya and Henrik I. Christensen},
  journal={Adaptive Behaviour},
In this article, we explore an evolutionary approach to the optimization of potential-based shaping rewards and meta-parameters in reinforcement learning. Shaping rewards is a frequently used approach to increase the learning performance of reinforcement learning, with regards to both initial performance and convergence speed. Shaping rewards provide additional knowledge to the agent in the form of richer reward signals, which guide learning to high-rewarding states. Reinforcement learning… CONTINUE READING


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