Summary of Differentially Private Reinforcement Learning with Self-play, by Dan Qiao et al.
Differentially Private Reinforcement Learning with Self-Play
by Dan Qiao, Yu-Xiang Wang
First submitted to arxiv on: 11 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Multiagent Systems (cs.MA); Machine Learning (stat.ML)
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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 A novel approach to multi-agent reinforcement learning (multi-agent RL) is proposed, incorporating differential privacy (DP) constraints to protect sensitive data in real-world applications. The study extends definitions of Joint DP (JDP) and Local DP (LDP) to two-player zero-sum episodic Markov Games, ensuring trajectory-wise privacy protection. A provably efficient algorithm based on optimistic Nash value iteration and privatization of Bernstein-type bonuses is designed, satisfying JDP and LDP requirements with appropriate privacy mechanisms. The regret bound generalizes the best known result under single-agent RL and reduces to the best known result for multi-agent RL without privacy constraints. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Multi-agent reinforcement learning with differential privacy is a new approach that helps protect sensitive data in real-life applications. Imagine you’re playing a game with someone, but you don’t want them to see your strategy. That’s what this study does – it creates an algorithm that makes sure both players’ moves are private while still winning the game. This is important because in the real world, people share their data for different reasons, and they want to keep some things private. |
Keywords
» Artificial intelligence » Reinforcement learning
