CH-MARL: A Multimodal Benchmark for Cooperative, Heterogeneous Multi-Agent Reinforcement Learning

Understand

We propose a multimodal (vision-and-language) benchmark for cooperative and heterogeneous multi-agent learning.

• We introduce a benchmark multimodal dataset with tasks involving collaboration between multiple simulated heterogeneous robots in a rich multi-room home environment.
• We provide an integrated learning framework, multimodal implementations of state-of-the-art multi-agent reinforcement learning techniques, and a consistent evaluation protocol.
• Our experiments investigate the impact of different modalities on multi-agent learning performance.