NePPO: Near-Potential Policy Optimization for General-Sum Multi-Agent Reinforcement Learning

2026-03-07

AI Summary

This work addresses key challenges in multi-agent reinforcement learning for general-sum games, including unstable learning dynamics, lack of convergence guarantees, and difficulty in defining system-level objectives. The authors propose NePPO, a novel method that introduces an agent-agnostic, learnable common potential function to approximate the original game as a cooperative one, thereby constructing a new multi-agent optimization objective that provably approximates a Nash equilibrium. NePPO is trained using a zeroth-order gradient descent scheme integrated within a policy optimization framework. Empirical evaluations across diverse mixed cooperative–competitive environments demonstrate that NePPO significantly outperforms established baselines such as MAPPO, IPPO, and MADDPG.

Technology Category

• Multiagent Systems: Multiagent Learning
• Search and Optimization: Learning to Search
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Multi-agent reinforcement learning (MARL) is increasingly used to design learning-enabled agents that interact in shared environments. However, training MARL algorithms in general-sum games remains challenging: learning dynamics can become unstable, and convergence guarantees typically hold only in restricted settings such as two-player zero-sum or fully cooperative games. Moreover, when agents have heterogeneous and potentially conflicting preferences, it is unclear what system-level objective should guide learning. In this paper, we propose a new MARL pipeline called Near-Potential Policy Optimization (NePPO) for computing approximate Nash equilibria in mixed cooperative--competitive environments. The core idea is to learn a player-independent potential function such that the Nash equilibrium of a cooperative game with this potential as the common utility approximates a Nash equilibrium of the original game. To this end, we introduce a novel MARL objective such that minimizing this objective yields the best possible potential function candidate and consequently an approximate Nash equilibrium of the original game. We develop an algorithmic pipeline that minimizes this objective using zeroth-order gradient descent and returns an approximate Nash equilibrium policy. We empirically show the superior performance of this approach compared to popular baselines such as MAPPO, IPPO and MADDPG.

Problem

• Multi-Agent Reinforcement Learning
• General-Sum Games
• Nash Equilibrium
• Mixed Cooperative-Competitive Environments
• Learning Stability

Innovation

• Near-Potential Policy Optimization
• General-Sum Games
• Potential Function
• Nash Equilibrium
• Multi-Agent Reinforcement Learning