A new article discusses the challenges of planning with learned models, emphasizing that any model used for planning is inherently imperfect. It proposes strategies for robust planning under uncertainty, such as Model Predictive Control (MPC), which involves extensive planning but minimal commitment, and highlights techniques like Cross-Entropy Method (CEM) and random shooting with defined budgets. The piece also differentiates between aleatoric and epistemic uncertainty and introduces the concept of expected free energy. AI
IMPACT Discusses fundamental challenges in AI planning, suggesting methods to improve robustness when dealing with imperfect learned models.
RANK_REASON The item is a blog post discussing AI planning concepts, not a primary release or significant industry event.
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- model predictive control
- Planning under Uncertainty in Constrained and Partially Observable Environments
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