<ul><li>Reinforcement learning (RL) systems are challenged by complex real-world scenarios requiring coordination between multiple agents.</li><li>In a recent study, researchers show that employing an inference phase at execution time, along with a specific inference strategy, can help break the performance ceiling in multi-agent RL problems.</li><li>The research results indicate up to a 126% improvement over previous state-of-the-art performance across 17 tasks with a few seconds of additional wall-clock time during execution.</li><li>The study includes over 60k experiments and provides promising compute scaling properties, making it the most extensive research on inference strategies for complex RL to date.</li></ul>

Breaking the Performance Ceiling in Complex Reinforcement Learning requires Inference Strategies

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