Hyperbolically-Discounted Reinforcement Learning on Reward-Punishment Framework

06/03/2021
by   Taisuke Kobayashi, et al.
0

This paper proposes a new reinforcement learning with hyperbolic discounting. Combining a new temporal difference error with the hyperbolic discounting in recursive manner and reward-punishment framework, a new scheme to learn the optimal policy is derived. In simulations, it is found that the proposal outperforms the standard reinforcement learning, although the performance depends on the design of reward and punishment. In addition, the averages of discount factors w.r.t. reward and punishment are different from each other, like a sign effect in animal behaviors.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset