Planning & Scheduling
Monte-Carlo Tree Search for Constrained POMDPs
Jongmin Lee, Geon-hyeong Kim, Pascal Poupart, Kee-Eung Kim
Neural Information Processing Systems http://nips.cc/
Single-Agent Policy Tree Search With Guarantees
Laurent Orseau, Levi Lelis, Tor Lattimore, Theophane Weber
Neural Information Processing Systems http://nips.cc/
ExplainableReinforcementLearningviaModel Transforms
Understanding emerging behaviors of reinforcement learning (RL) agents may be difficult since such agents are often trained in complex environments using highly complex decision making procedures.
Learning Plannable Representations with Causal InfoGAN
Thanard Kurutach, Aviv Tamar, Ge Yang, Stuart J. Russell, Pieter Abbeel
Asanexample,consider designing a logical representation for the state of a deformable object such as a rope.
Planning in entropy-regularized Markov decision processes and games
Jean-Bastien Grill, Omar Darwiche Domingues, Pierre Menard, Remi Munos, Michal Valko
Neural Information Processing Systems http://nips.cc/
Hierarchical Reinforcement Learning for Zero-shot Generalization with Subtask Dependencies
Sungryull Sohn, Junhyuk Oh, Honglak Lee
Neural Information Processing Systems http://nips.cc/
Leveraging Environment Interaction for Automated PDDL Translation and Planning with Large Language Models
We achieve an average task solve rate of 66% compared to a 29% solve rate by GPT -4's intrinsic planning with chain-of-thought prompting.