How can I rank stable multi-agent strategies in dynamic games?
Ranking Joint Policies in Dynamic Games using Evolutionary Dynamics
February 21, 2025
https://arxiv.org/pdf/2502.14724This paper proposes a method for ranking the effectiveness of different "styles of play" (strategies) that AI agents can employ in dynamic, multi-agent games, using the graph coloring problem as a test case. It uses a combination of deep reinforcement learning (to train agents enacting different strategies), empirical game theory (to analyze the interactions between strategies), and an evolutionary algorithm called α-Rank (to rank the long-term stability and performance of strategy combinations).
Key points for LLM-based multi-agent systems:
- Focus on styles of play: The research emphasizes analyzing and ranking distinct strategies rather than individual actions, which aligns with the concept of using LLMs to embody different personas or approaches in multi-agent interactions.
- Dynamic environments: The approach addresses scenarios where the game state changes not just through agent actions but also environmental factors, making it relevant to complex, real-world applications of LLMs.
- Empirical game analysis: By simulating interactions and creating an empirical payoff matrix, the method can analyze complex games where it's hard to define rules explicitly, reflecting the difficulty of pre-defining LLM behavior.
- Evolutionary ranking: α-Rank offers a way to evaluate the long-term success of different strategy combinations, suggesting it could be used to assess the stability and effectiveness of various LLM interaction patterns.
- Transparency through response graphs: The generated response graphs provide a visual way to understand the dynamics between strategies, which could help in analyzing and interpreting complex LLM interactions.