Approximating Shapley Explanations in Reinforcement Learning

Reinforcement learning has achieved remarkable success in complex decision-making environments, yet its lack of transparency limits its deployment in practice, especially in safety-critical settings. Shapley values from cooperative game theory provide a principled framework for explaining reinforcement learning; however, the computational cost of Shapley explanations is an obstacle to their use. We introduce FastSVERL, a scalable method for explaining reinforcement learning by approximating Shapley values. FastSVERL is designed to handle the unique challenges of reinforcement learning, including temporal dependencies across multi-step trajectories, learning from off-policy data, and adapting to evolving agent behaviours in real time. FastSVERL introduces a practical, scalable approach for principled and rigorous interpretability in reinforcement learning.

A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values

Reinforcement learning agents can achieve superhuman performance, but their decisions are often difficult to interpret. This lack of transparency limits deployment, especially in safety-critical settings where human trust and accountability are essential. In this work, we develop a theoretical framework for explaining reinforcement learning through the influence of state features, which represent what the agent observes in its environment. We identify three core elements of the agent-environment interaction that benefit from explanation: behaviour (what the agent does), performance (what the agent achieves), and value estimation (what the agent expects to achieve). We treat state features as players cooperating to produce each element and apply Shapley values, a principled method from cooperative game theory, to identify the influence of each feature. This approach yields a family of mathematically grounded explanations with clear semantics and theoretical guarantees. We use illustrative examples to show how these explanations align with human intuition and reveal novel insights. Our framework unifies and extends prior work, making explicit the assumptions behind existing approaches, and offers a principled foundation for more interpretable and trustworthy reinforcement learning.

Explaining Reinforcement Learning with Shapley Values

For reinforcement learning systems to be widely adopted, their users must understand and trust them. We present a theoretical analysis of explaining reinforcement learning using Shapley values, following a principled approach from game theory for identifying the contribution of individual players to the outcome of a cooperative game. We call this general framework Shapley Values for Explaining Reinforcement Learning (SVERL). Our analysis exposes the limitations of earlier uses of Shapley values in reinforcement learning. We then develop an approach that uses Shapley values to explain agent performance. In a variety of domains, SVERL produces meaningful explanations that match and supplement human intuition.