SUN: Reaching for Novelty in Reinforcement Learning
TL;DR
This paper proposes SUccessor-to-Novelty (SUN), an indicator derived from successor value functions to identify goals that are both novel and reachable and presents an adaptive goal-selection strategy that leverages these properties, and an accurate yet lightweight pseudocount to avoid the overhead of classic methods.
Abstract
Exploration in reinforcement learning (RL) remains a fundamental challenge. Recent goal-conditioned RL strategies (which select goals to encourage broader state coverage) have shown promising results, but none scores a goal by novelty and reachability jointly: the two signals are traded off by hand, applied in sequence, or one is neglected outright. In this paper, we introduce a reachability-aware goal-selection framework that explicitly integrates these two aspects, and that can be seamlessly incorporated into any off-policy RL algorithm. To this aim, we propose SUccessor-to-Novelty (SUN), an indicator derived from successor value functions to identify goals that are both novel and reachable. We prove that SUN is the value of a count-scaled goal-reaching reward, bounds short-horizon hitting probabilities, and rejects unreachable goals. We further present an adaptive goal-selection strategy that leverages these properties, along with an accurate yet lightweight pseudocount to avoid the overhead of classic methods. We back up all our claims with thorough benchmarks: SUN consistently outperforms state-of-the-art methods in standard and novel environments with unreachable or hard-to-reach states, irreversible transitions, obstacles, mazes, and unbounded spaces. Code at https://github.com/sparisi/rl-anvil
