# Hyperbolically-Discounted Reinforcement Learning on Reward-Punishment Framework

arXiv: https://arxiv.org/abs/2106.01516 · HTML: https://mlsanity.com/paper/2106.01516/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 0 of its 7 references that are in the map.
- First posted on arXiv 3 Jun 2021.

## Abstract (arXiv)

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.