HackAtari: Atari Learning Environments for Robust and Continual Reinforcement Learning

Understand

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.