标题： HackAtari：用于鲁棒和持续强化学习的Atari学习环境 显示英文标题

标题： HackAtari: Atari Learning Environments for Robust and Continual Reinforcement Learning

摘要： 人工代理对新情况的适应能力和与预期行为的一致性对于其有效部署至关重要。 强化学习（RL）利用新奇性作为探索的手段，但代理通常难以处理新情况，这阻碍了泛化能力。 为了解决这些问题，我们提出了HackAtari，一个引入受控新奇性的框架，应用于最常用的RL基准，即Atari学习环境。 HackAtari使我们能够创建新的游戏场景（包括简化以用于课程学习），交换游戏元素的颜色，并为代理引入不同的奖励信号。 我们证明了在原始环境中训练的当前代理存在鲁棒性失败，并通过使用C51和PPO的实验评估了HackAtari在增强RL代理的鲁棒性和对齐行为方面的有效性。 总体而言，HackAtari可用于提高当前和未来RL算法的鲁棒性，允许神经符号RL、课程RL、因果RL以及LLM驱动的RL。 我们的工作强调了在RL代理中开发可解释性的的重要性。 显示英文摘要

摘要： Artificial agents' adaptability to novelty and alignment with intended behavior is crucial for their effective deployment. Reinforcement learning (RL) leverages novelty as a means of exploration, yet agents often struggle to handle novel situations, hindering generalization. To address these issues, we propose HackAtari, a framework introducing controlled novelty to the most common RL benchmark, the Atari Learning Environment. HackAtari allows us to create novel game scenarios (including simplification for curriculum learning), to swap the game elements' colors, as well as to introduce different reward signals for the agent. We demonstrate that current agents trained on the original environments include robustness failures, and evaluate HackAtari's efficacy in enhancing RL agents' robustness and aligning behavior through experiments using C51 and PPO. Overall, HackAtari can be used to improve the robustness of current and future RL algorithms, allowing Neuro-Symbolic RL, curriculum RL, causal RL, as well as LLM-driven RL. Our work underscores the significance of developing interpretable in RL agents.