NeurIPS 2024 - Posters and Hallways 1
TalkRL: The Reinforcement Learning Podcast
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Unifying Multi-Agent Reinforcement Learning through Standardization
This chapter delves into a capacity model designed for enhanced control and optimization in multi-agent reinforcement learning. It emphasizes the importance of reducing fragmentation by developing a cohesive library for algorithm and task customization, along with the introduction of benchmarks to facilitate research comparisons.
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