On Generating Explanations for Reinforcement Learning Policies: An Empirical Study
This addresses the need for human-comprehensible explanations of RL policies, which is incremental as it builds on existing explanation methods.
The paper tackled the problem of explaining reinforcement learning policies by introducing linear temporal logic formulae and a search algorithm to find the best explanation, focusing on objectives and prerequisites, with effectiveness demonstrated in simulated capture-the-flag and car-parking environments.
Understanding a \textit{reinforcement learning} policy, which guides state-to-action mappings to maximize rewards, necessitates an accompanying explanation for human comprehension. In this paper, we introduce a set of \textit{linear temporal logic} formulae designed to provide explanations for policies, and an algorithm for searching through those formulae for the one that best explains a given policy. Our focus is on explanations that elucidate both the ultimate objectives accomplished by the policy and the prerequisite conditions it upholds throughout its execution. The effectiveness of our proposed approach is illustrated through a simulated game of capture-the-flag and a car-parking environment,