Uncertainty is ubiquitous in games, both in the agents playing games and often in the games
themselves. Working with uncertainty is therefore an important component of successful …

Abstract Model-based reinforcement learning (RL) algorithms, which learn a dynamics
model from logged experience and perform conservative planning under the learned model …

Recent work has shown that supervised learning alone, without temporal difference (TD)
learning, can be remarkably effective for offline RL. When does this hold true, and which …

Offline reinforcement learning (RL) defines the task of learning from a static logged dataset
without continually interacting with the environment. The distribution shift between the …

Actor-critic methods are widely used in offline reinforcement learningpractice, but are not so
well-understood theoretically. We propose a newoffline actor-critic algorithm that naturally …

Offline Reinforcement Learning (RL) aims to learn policies from previously collected
datasets without exploring the environment. Directly applying off-policy algorithms to offline …

Reinforcement learning from human feedback (RLHF) is a standard approach for fine-tuning
large language models to follow instructions. As part of this process, learned reward models …

This paper studies the theoretical framework of the alignment process of generative models
with Reinforcement Learning from Human Feedback (RLHF). We consider a standard …

Offline reinforcement learning (RL) methods can generally be categorized into two types: RL-
based and Imitation-based. RL-based methods could in principle enjoy out-of-distribution …

Robotic applications often involve working in environments that are uncertain, dynamic, and
partially observable. Recently, diffusion models have been proposed for learning trajectory …