A unified uncertainty-aware exploration: Combining epistemic and aleatory uncertainty
This addresses the problem of instability in risk-sensitive exploration for reinforcement learning practitioners, though it is incremental as it builds on distributional RL.
The paper tackles the challenge of exploration in reinforcement learning by proposing a unified method to combine epistemic and aleatory uncertainty, which outperforms existing approaches on tasks with exploration and risk challenges.
Exploration is a significant challenge in practical reinforcement learning (RL), and uncertainty-aware exploration that incorporates the quantification of epistemic and aleatory uncertainty has been recognized as an effective exploration strategy. However, capturing the combined effect of aleatory and epistemic uncertainty for decision-making is difficult. Existing works estimate aleatory and epistemic uncertainty separately and consider the composite uncertainty as an additive combination of the two. Nevertheless, the additive formulation leads to excessive risk-taking behavior, causing instability. In this paper, we propose an algorithm that clarifies the theoretical connection between aleatory and epistemic uncertainty, unifies aleatory and epistemic uncertainty estimation, and quantifies the combined effect of both uncertainties for a risk-sensitive exploration. Our method builds on a novel extension of distributional RL that estimates a parameterized return distribution whose parameters are random variables encoding epistemic uncertainty. Experimental results on tasks with exploration and risk challenges show that our method outperforms alternative approaches.