MARL-BattleGrounds: A Novel JAX-native Benchmark for Heterogeneous and Competitive Multi-agent RL
We present MARL-BattleGrounds (MARL-BGs), an easy-to-use JAX-native benchmark for heterogeneous and competitive multi-agent reinforcement learning (MARL), designed to be sample-efficient, computationally efficient, and researcher-centric.
ProofPaper ↗
Key points
- MARL benchmarks are typically simple and efficient, like SMAX, where winning reduces to unit micromanagement, or rich and costly, like Dota 2.
- Its first mode, Team Deathmatch, is a simultaneous turn-based game between two teams of five heterogeneous agents.
- All entrants are publicly released as downloadable opponents.
- We benchmark MARL-BGs with six additional deep MARL baselines, which we release.
Sources (1)
- [1]MARL-BattleGrounds: A Novel JAX-native Benchmark for Heterogeneous and Competitive Multi-agent RLarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 10, 12:57 PM
We present MARL-BattleGrounds (MARL-BGs), an easy-to-use JAX-native benchmark for heterogeneous and competitive multi-agent reinforcement learning (MARL), designed to be sample-efficient, computationally efficient, and researcher-centric.
MARL benchmarks are typically simple and efficient, like SMAX, where winning reduces to unit micromanagement, or rich and costly, like Dota 2.
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