Learning from Reward-Free Offline Data: A Case for Planning with Latent Dynamics Models

arXiv — cs.LG•Thursday, October 30, 2025 at 4:00:00 AM

A recent study discusses the potential of using reward-free offline data to enhance planning in artificial intelligence through latent dynamics models. This approach could bridge the gap between reinforcement learning and optimal control, allowing AI agents to tackle tasks in unfamiliar environments more effectively. Understanding these dynamics is crucial as it could lead to more robust AI systems capable of adapting to new challenges without extensive retraining.

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