Proto-value functions: developmental reinforcement learning

  title={Proto-value functions: developmental reinforcement learning},
  author={Sridhar Mahadevan},
This paper presents a novel framework called proto-reinforcement learning (PRL), based on a mathematical model of a proto-value function: these are task-independent basis functions that form the building blocks of all value functions on a given state space manifold. Proto-value functions are learned not from rewards, but instead from analyzing the topology of the state space. Formally, proto-value functions are Fourier eigenfunctions of the Laplace-Beltrami diffusion operator on the state space… CONTINUE READING
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