Researchers introduce efxplorer for Cooperative Multi-Agent reinforcement learning
A team of researchers has proposed EFXPLORER, a new framework for multi-agent reinforcement learning (MARL). The method aims to address the challenge of achieving cooperative behavior by balancing exploration and task performance. Unlike traditional approaches that combine intrinsic rewards with team objectives, EFXPLORER uses an epigraph reformulation to separate these elements and regulate…
Key points
- EFXPLORER uses epigraph reformulation to separate intrinsic rewards from task objectives, regulating exploration via adaptive budgets
- Method outperforms baselines in SMAX, VMAS, and MPE benchmark suites, per the authors
- Successor distance-based intrinsic reward encourages diverse and temporally extended exploration
The paper claims EFXPLORER outperforms existing baselines across three benchmark suites: SMAX, VMAS, and MPE. The method introduces a successor distance-based intrinsic reward to encourage diverse and long-horizon exploration. The authors argue that this approach mitigates the risk of distorting learning signals that can arise from naive combinations of intrinsic and extrinsic rewards.
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