Adapting to Reward Progressivity via Spectral Reinforcement Learning

TL;DR

This paper introduces Spectral DQN, a novel reinforcement learning method that decomposes rewards into frequencies to better handle tasks with increasing reward magnitudes over time, improving performance in challenging domains.

Contribution

Spectral DQN is a new approach that decomposes rewards into frequency components, balancing training loss and enhancing learning in reward-progressive tasks.

Findings

Outperforms standard methods in reward-progressive domains

Remains competitive on standard Atari games

Shows potential advantages beyond its initial focus

Abstract

In this paper we consider reinforcement learning tasks with progressive rewards; that is, tasks where the rewards tend to increase in magnitude over time. We hypothesise that this property may be problematic for value-based deep reinforcement learning agents, particularly if the agent must first succeed in relatively unrewarding regions of the task in order to reach more rewarding regions. To address this issue, we propose Spectral DQN, which decomposes the reward into frequencies such that the high frequencies only activate when large rewards are found. This allows the training loss to be balanced so that it gives more even weighting across small and large reward regions. In two domains with extreme reward progressivity, where standard value-based methods struggle significantly, Spectral DQN is able to make much farther progress. Moreover, when evaluated on a set of six standard Atari games that do not overtly favour the approach, Spectral DQN remains more than competitive: While it underperforms one of the benchmarks in a single game, it comfortably surpasses the benchmarks in three games. These results demonstrate that the approach is not overfit to its target problem, and suggest that Spectral DQN may have advantages beyond addressing reward progressivity.