Summary of Hackatari: Atari Learning Environments For Robust and Continual Reinforcement Learning, by Quentin Delfosse et al.
HackAtari: Atari Learning Environments for Robust and Continual Reinforcement Learning
by Quentin Delfosse, Jannis Blüml, Bjarne Gregori, Kristian Kersting
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed HackAtari framework introduces controlled novelty to the Atari Learning Environment, a common reinforcement learning (RL) benchmark, to improve the adaptability and alignment of artificial agents with intended behavior. The framework allows for novel game scenarios, color swapping, and reward signal modification, which are demonstrated to enhance robustness and align behavior in experiments using C51 and PPO algorithms. This work highlights the importance of developing interpretable RL agents and has implications for Neuro-Symbolic RL, curriculum RL, causal RL, and LLM-driven RL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary HackAtari is a new way to help artificial agents learn and adapt by introducing new and unexpected situations in games. Right now, these agents struggle to handle surprises, which makes them less useful in real-world applications. The HackAtari framework solves this problem by allowing us to create new game scenarios that are a mix of old and new elements. We tested this approach using two different algorithms (C51 and PPO) and found that it significantly improves the performance and robustness of these agents. |
Keywords
» Artificial intelligence » Alignment » Reinforcement learning
