Papers › EXPODE: EXploiting POlicy Discrepancy for Efficient Exploration in Multi-agent...
EXPODE: EXploiting POlicy Discrepancy for Efficient Exploration in Multi-agent Reinforcement Learning
Yucong Zhang, Chao Yu
Recently, Multi-Agent Reinforcement Learning (MARL) has been applied to a large number of scenarios and has shown promising performance. However, existing MARL algorithms still suffer from the severe exploration problem. In this paper, we propose EXploiting POlicy Discrepancy for efficient Exploration (EXPODE), a new multi-agent exploration framework that leverages discrepancy between two different policies to enable the agents to explore the environment more efficiently. In addition, to tackle the mutual influence issue caused by the concurrent exploration of the agents, we propose three different mechanisms to coordinate the agents’ exploration by taking the information of other agents’ states and policies into account when measuring the agent-wise policy discrepancies. Experimental results on three challenging tasks, i.e., Predator Prey, StarCraft II micromanagement tasks, and Google Research Football, demonstrate that EXPODE achieves the state-of-the-art performance.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections