planning algorithm
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Occam's razor is insufficient to infer the preferences of irrational agents
Stuart Armstrong, Sören Mindermann
Toaddressthis, we need simple'normative' assumptions, which cannot be deduced exclusively fromobservations.
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PALMER: Perception-ActionLoopwithMemory forLong-HorizonPlanning
This creates a tight feedback loop between representation learning,memory,reinforcementlearning,andsampling-basedplanning.
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Thinker: Learning to Plan and Act
Stephen Chung, Ivan Anokhin, David Krueger
We demonstrate the algorithm's effectiveness
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6af779991368999ab3da0d366c208fba-Paper-Conference.pdf
Planning enables autonomous agents to solve complex decision-making problems by evaluating predictions of the future. However, classical planning algorithms often become infeasible in real-world settings where state spaces are high-dimensional andtransitiondynamicsunknown.
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