Summary of On Minimizing Adversarial Counterfactual Error in Adversarial Rl, by Roman Belaire et al.
On Minimizing Adversarial Counterfactual Error in Adversarial RL
by Roman Belaire, Arunesh Sinha, Pradeep Varakantham
First submitted to arxiv on: 7 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper addresses the issue of adversarial noise in observations for Deep Reinforcement Learning (DRL) policies. Adversarial perturbations can significantly impact safety-critical scenarios by altering the information observed by the agent, making the state partially observable. Existing approaches either enforce consistent actions across nearby states or maximize the worst-case value within adversarially perturbed observations. However, these methods suffer from performance degradation when attacks succeed or are overly conservative in benign settings. To address this, the authors introduce Adversarial Counterfactual Error (ACoE), which balances value optimization with robustness by accounting for partial observability directly. They also propose Cumulative-ACoE (C-ACoE) as a theoretically-grounded surrogate objective for model-free settings. The paper evaluates these methods on standard benchmarks, including MuJoCo, Atari, and Highway, demonstrating significant improvements over current state-of-the-art approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about making artificial intelligence systems more reliable when they’re faced with fake or misleading information. Right now, these AI systems are vulnerable to attacks that can make them do the wrong thing. The authors of this paper want to fix this problem by creating a new way for the AI system to think about what’s really going on, even when some of the information it gets is fake. They tested their idea on several different types of problems and found that it worked much better than existing methods. |
Keywords
* Artificial intelligence * Optimization * Reinforcement learning
