Undirected Networks
Schema-learning and rebinding as mechanisms of in-context learning and emergence
In-context learning (ICL) is one of the most powerful and most unexpected capabilities to emerge in recent transformer-based large language models (LLMs).
On Markov Chain Gradient Descent
Tao Sun, Yuejiao Sun, Wotao Yin
Neural Information Processing Systems http://nips.cc/
LearningRewardMachinesforPartially ObservableReinforcementLearning
RL agents learn policies from experience.
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/
Towards Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling
Tengyang Xie, Yifei Ma, Yu-Xiang Wang
Solving OPE is often the starting point in many RL applications. To tackle the problem of OPE, the idea of importance sampling (IS) corrects the mismatch in the distributions under the behavior policy and target policy.
