Hindsight-DICE: Stable Credit Assignment for Deep Reinforcement Learning

TL;DR

Hindsight-DICE introduces a stable, efficient method for credit assignment in deep reinforcement learning by adapting importance-sampling techniques, improving learning stability and performance in environments with sparse rewards.

Contribution

The paper develops a novel importance-sampling based approach to stabilize and enhance hindsight policy methods for better credit assignment in complex environments.

Findings

Improved stability in credit assignment tasks.

Enhanced learning efficiency in sparse reward environments.

Broader applicability across diverse reinforcement learning scenarios.

Abstract

Oftentimes, environments for sequential decision-making problems can be quite sparse in the provision of evaluative feedback to guide reinforcement-learning agents. In the extreme case, long trajectories of behavior are merely punctuated with a single terminal feedback signal, leading to a significant temporal delay between the observation of a non-trivial reward and the individual steps of behavior culpable for achieving said reward. Coping with such a credit assignment challenge is one of the hallmark characteristics of reinforcement learning. While prior work has introduced the concept of hindsight policies to develop a theoretically moxtivated method for reweighting on-policy data by impact on achieving the observed trajectory return, we show that these methods experience instabilities which lead to inefficient learning in complex environments. In this work, we adapt existing importance-sampling ratio estimation techniques for off-policy evaluation to drastically improve the stability and efficiency of these so-called hindsight policy methods. Our hindsight distribution correction facilitates stable, efficient learning across a broad range of environments where credit assignment plagues baseline methods.