Summary of Robust Off-policy Reinforcement Learning Via Soft Constrained Adversary, by Kosuke Nakanishi et al.
Robust off-policy Reinforcement Learning via Soft Constrained Adversary
by Kosuke Nakanishi, Akihiro Kubo, Yuji Yasui, Shin Ishii
First submitted to arxiv on: 31 Aug 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 The abstract discusses recent advancements in robust reinforcement learning (RL) against input observations, highlighting limitations in current methods when considering adversaries with long-term horizons. Specifically, it notes that mutual dependencies between policies and optimal adversaries restrict the development of off-policy RL algorithms. Additionally, existing approaches assume perturbations based on the Lp-norm, neglecting prior knowledge of the perturbation distribution. The paper introduces an f-divergence constrained problem incorporating prior knowledge distribution, deriving two typical attacks and robust learning frameworks. Evaluation results demonstrate excellent performance in sample-efficient off-policy RL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Robust reinforcement learning is a type of AI that helps machines learn from experiences without being tricked by fake or misleading information. This paper talks about ways to make these machines more resistant to bad data. It highlights two main problems with current approaches: they rely too heavily on the machine’s own performance, and they assume all mistakes are random and unpredictable. The authors propose a new way of thinking about this problem that takes into account what we already know about how errors work in the environment. They show that their approach can help machines learn faster and better when faced with unexpected challenges. |
Keywords
» Artificial intelligence » Reinforcement learning
