  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.
