New RL method teaches AI agents when to plan or react

Researchers have developed a new reinforcement learning method to train artificial agents in meta-reasoning, enabling them to decide between fast, reactive decision-making and slower, deliberative planning. This approach uses a meta-reasoning policy that predicts when a reactive policy might perform poorly, signaling the need for more computation through planning. Experiments in motion planning and navigation environments demonstrated that this system can effectively learn when to engage in planning versus relying on the reactive policy, and it adapts to improve the reactive policy's performance. AI

IMPACT This research could lead to more efficient and adaptable AI systems capable of optimizing computational resources for decision-making.

RANK_REASON The cluster contains a research paper detailing a new method for artificial agents. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• artificial intelligence
• arXiv
• imitation learning
• Meta-Reasoning: Monitoring and Control of Thinking and Reasoning
• motion planning
• navigation environments
• reinforcement learning
• When to Plan: Learning to Select Between Reactive Control and Deliberative Planning

• paper
• model release

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