Summary of On the Perturbed States For Transformed Input-robust Reinforcement Learning, by Tung M. Luu et al.
On the Perturbed States for Transformed Input-robust Reinforcement Learning
by Tung M. Luu, Haeyong Kang, Tri Ton, Thanh Nguyen, Chang D. Yoo
First submitted to arxiv on: 31 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 novel method, Transformed Input-robust RL (TIRL), aims to alleviate the vulnerability of reinforcement learning (RL) agents to adversarial perturbations in input observations during deployment. By employing input transformation-based defenses, TIRL explores an alternative avenue for mitigating the impact of adversaries. The method introduces two principles: autoencoder-styled denoising and bounded transformations (bit-depth reduction and vector quantization). These transformations are applied to the state before feeding it into the policy network. Extensive experiments on multiple MuJoCo environments demonstrate that input transformation-based defenses, specifically VQ, effectively defend against several adversaries in state observations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement learning agents can be vulnerable when they’re deployed in real-world situations because of bad data. To make them more robust, some researchers have developed training methods that help agents learn to handle these issues. This paper proposes a new approach called TIRL (Transformed Input-robust RL) that uses different techniques to improve the reliability of these agents. The main idea is to change the input data in certain ways before it reaches the agent’s decision-making system. This can help the agent learn to make better decisions even when the data is flawed. In experiments, TIRL was tested on several scenarios and showed promising results. |
Keywords
* Artificial intelligence * Autoencoder * Quantization * Reinforcement learning
