How can I incentivize agents to explore better together?
PIMAEX: Multi-Agent Exploration through Peer Incentivization
January 3, 2025
https://arxiv.org/pdf/2501.01266This paper introduces PIMAEX (Peer-Incentivized Multi-Agent Exploration), a novel reward function designed to improve exploration in multi-agent reinforcement learning scenarios, particularly those with sparse or deceptive rewards. PIMAEX encourages agents to influence each other toward discovering novel states by rewarding them for leading peers to unexplored areas. This is combined with PIMAEX-Communication, an algorithm allowing agents to communicate via discrete messages, enabling them to learn coordinated exploration strategies.
Key points for LLM-based multi-agent systems:
- Social Influence for Exploration: The concept of rewarding agents based on their influence on others' exploration opens new avenues for steering LLM agents toward novel text generation or problem-solving strategies. This could be valuable in creative writing, code generation, or collaborative problem-solving scenarios.
- Discrete Communication Channel: The use of discrete messages for communication simplifies implementation and could be adapted to various LLM-based communication protocols, allowing agents to exchange structured information, hints, or feedback.
- Intrinsic Curiosity & Novelty: PIMAEX incorporates intrinsic curiosity rewards, a concept readily applicable to LLMs. Rewarding LLMs for generating novel or surprising text outputs could encourage creativity and diversity in generation.
- Counterfactual Reasoning: The use of counterfactual reasoning to assess influence helps isolate the impact of individual agents, providing a cleaner signal for learning and reward attribution in complex multi-agent settings. This could be applied to evaluate the contribution of individual LLMs in collaborative tasks.
- Partially Observable Environments: The Consume/Explore environment used for evaluation is partially observable, reflecting the common scenario where LLMs have limited access to the overall system state. The success of PIMAEX in this setting demonstrates its potential for real-world LLM applications.