TY - JOUR AU - Pocius, Rey AU - Neal, Lawrence AU - Fern, Alan PY - 2019/07/17 Y2 - 2024/03/28 TI - Strategic Tasks for Explainable Reinforcement Learning JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 33 IS - 01 SE - Student Abstract Track DO - 10.1609/aaai.v33i01.330110007 UR - https://ojs.aaai.org/index.php/AAAI/article/view/5140 SP - 10007-10008 AB - <p>Commonly used sequential decision making tasks such as the games in the Arcade Learning Environment (ALE) provide rich observation spaces suitable for deep reinforcement learning. However, they consist mostly of low-level control tasks which are of limited use for the development of explainable artificial intelligence(XAI) due to the fine temporal resolution of the tasks. Many of these domains also lack built-in high level abstractions and symbols. Existing tasks that provide for both strategic decision-making and rich observation spaces are either difficult to simulate or are intractable. We provide a set of new strategic decision-making tasks specialized for the development and evaluation of explainable AI methods, built as constrained mini-games within the StarCraft II Learning Environment.</p> ER -