Summary of Safe Multi-agent Reinforcement Learning with Convergence to Generalized Nash Equilibrium, by Zeyang Li et al.
Safe Multi-Agent Reinforcement Learning with Convergence to Generalized Nash Equilibrium
by Zeyang Li, Navid Azizan
First submitted to arxiv on: 22 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY)
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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 Multi-agent reinforcement learning (MARL) has achieved significant success in cooperative tasks, showcasing impressive performance and scalability. However, deploying MARL agents in real-world applications presents critical safety challenges. Current safe MARL algorithms are largely based on the constrained Markov decision process (CMDP) framework, which enforces constraints only on discounted cumulative costs and lacks an all-time safety assurance. To address these challenges, we propose a novel theoretical framework for safe MARL with state-wise constraints, where safety requirements are enforced at every state the agents visit. We develop a multi-agent method for identifying controlled invariant sets (CISs), ensuring convergence to a Nash equilibrium on the safety value function. This approach guarantees convergence to a generalized Nash equilibrium in state-wise constrained cooperative Markov games, achieving an optimal balance between feasibility and performance. Furthermore, we propose Multi-Agent Dual Actor-Critic (MADAC), a safe MARL algorithm that approximates the proposed iteration scheme within the deep RL paradigm. Empirical evaluations on safe MARL benchmarks demonstrate that MADAC consistently outperforms existing methods, delivering much higher rewards while reducing constraint violations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Researchers have made progress in teaming computers to work together effectively. However, there are still big challenges when using these teams in real-life situations. The current approaches to making sure the teams don’t cause harm can’t guarantee safety all the time. To solve this problem, we’re proposing a new way of thinking about teamwork that focuses on safety at every step. We’ve developed a method for identifying what’s safe and what’s not, which helps the team make better decisions. This approach ensures that the team doesn’t get stuck in a situation where it can’t do what’s right. To make this work in practice, we’re proposing an algorithm called MADAC that can be used with deep learning techniques. Our tests show that MADAC outperforms other methods while keeping safety in mind. |
Keywords
* Artificial intelligence * Deep learning * Reinforcement learning
