ReCCoVER: Detecting Causal Confusion for Explainable Reinforcement Learning
This addresses transparency issues in DRL for developers, but it is incremental as it builds on existing causal confusion concepts with specific environment tests.
The paper tackles the problem of causal confusion in deep reinforcement learning, where agents learn spurious correlations that hinder safe deployment, by proposing ReCCoVER to detect such confusion and suggest alternative features for decision-making, demonstrated in taxi and grid world environments.
Despite notable results in various fields over the recent years, deep reinforcement learning (DRL) algorithms lack transparency, affecting user trust and hindering their deployment to high-risk tasks. Causal confusion refers to a phenomenon where an agent learns spurious correlations between features which might not hold across the entire state space, preventing safe deployment to real tasks where such correlations might be broken. In this work, we examine whether an agent relies on spurious correlations in critical states, and propose an alternative subset of features on which it should base its decisions instead, to make it less susceptible to causal confusion. Our goal is to increase transparency of DRL agents by exposing the influence of learned spurious correlations on its decisions, and offering advice to developers about feature selection in different parts of state space, to avoid causal confusion. We propose ReCCoVER, an algorithm which detects causal confusion in agent's reasoning before deployment, by executing its policy in alternative environments where certain correlations between features do not hold. We demonstrate our approach in taxi and grid world environments, where ReCCoVER detects states in which an agent relies on spurious correlations and offers a set of features that should be considered instead.