Learning Transferable Domain Priors for Safe Exploration in Reinforcement Learning

TL;DR

This paper introduces a method to learn transferable domain priors that guide safe exploration in reinforcement learning, reducing risky actions and improving safety across similar tasks.

Contribution

It proposes a novel approach to identify undesirable actions and learn pseudo-reward based priors that are transferable and can be learned off-policy, enhancing safe exploration.

Findings

Safer exploration demonstrated in discrete and continuous environments.

Transferable priors improve safety in new tasks.

Theoretical analysis supports the effectiveness of the approach.

Abstract

Prior access to domain knowledge could significantly improve the performance of a reinforcement learning agent. In particular, it could help agents avoid potentially catastrophic exploratory actions, which would otherwise have to be experienced during learning. In this work, we identify consistently undesirable actions in a set of previously learned tasks, and use pseudo-rewards associated with them to learn a prior policy. In addition to enabling safer exploratory behaviors in subsequent tasks in the domain, we show that these priors are transferable to similar environments, and can be learned off-policy and in parallel with the learning of other tasks in the domain. We compare our approach to established, state-of-the-art algorithms in both discrete as well as continuous environments, and demonstrate that it exhibits a safer exploratory behavior while learning to perform arbitrary tasks in the domain. We also present a theoretical analysis to support these results, and briefly discuss the implications and some alternative formulations of this approach, which could also be useful in certain scenarios.