PowerMarketJax: A JAX Benchmark Suite for Multi-Agent Reinforcement Learning in Power Markets

arXiv:2609.37321 · cs.MA, cs.AI, cs.SY, eess.SY · Submitted 2026-09-29 · Read on arXiv

cs.MA, cs.AI, cs.SY, eess.SY

Submitted: 2026-09-29

Updated: 2026-09-29

Code: https://github.com/powermarketjax/PowerMarketJax

Terminology

• Benchmark — 29× in this paper · explained in 2 episodes
• Reinforcement Learning — 14× in this paper · explained in 4 episodes
• Actor — 10× in this paper · explained in 3 episodes
• Adam — 6× in this paper · explained in 2 episodes
• Physical Constraints — 5× in this paper · explained in 7 episodes
• Deep Reinforcement Learning — 2× in this paper · explained in 2 episodes
• Action Space — 1× in this paper · explained in 1 episode
• Agent-Based Simulation — 1× in this paper · explained in 1 episode
• Agent-based modeling — 1× in this paper · explained in 1 episode
• Communication Overhead — 1× in this paper · explained in 2 episodes
• Cooperation — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Mixed-Integer Linear Program (MILP) — 1× in this paper · explained in 2 episodes
• Multi-Agent Reinforcement Learning (MARL) — 1× in this paper · explained in 4 episodes
• Nanyang Technological University — 1× in this paper
• Nash Equilibrium — 1× in this paper · explained in 2 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes

Sources

• Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?
• Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning

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