Physical Sciences and Mathematics Commons^™

Adaptive Discounting In Reinforcement Learning, Milan Zinzuvadiya

Master's Theses

In Markov Decision Process (MDP) models of sequential decision-making, it is common practice to account for temporal discounting by incorporating a constant discount factor. While the effectiveness of fixed-rate discounting in various Reinforcement Learning (RL) settings is well-established, the efficiency of this scheme has been questioned in recent studies. Another notable shortcoming of fixed-rate discounting stems from abstracting away the experiential information of the agent, which is shown to be a significant component of delay discounting in human cognition. To address this issue, this thesis proposes a novel method for adaptive discounting entitled State-wise Adaptive Discounting from Experience (SADE). This …