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 …

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 …

Offline reinforcement learning (RL) provides a promising direction to exploit massive amount
of offline data for complex decision-making tasks. Due to the distribution shift issue, current …

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 …

Wastewater treatment process control optimization is a complex task in a highly nonlinear
environment. Reinforcement learning (RL) is a machine learning technique that stands out …

We investigate the problem of corruption robustness in offline reinforcement learning (RL)
with general function approximation, where an adversary can corrupt each sample in the …

In this paper, we study offline Reinforcement Learning with Human Feedback (RLHF) where
we aim to learn the human's underlying reward and the MDP's optimal policy from a set of …

For offline reinforcement learning (RL), model-based methods are expected to be data-
efficient as they incorporate dynamics models to generate more data. However, due to …

In this work, we decouple the iterative bi-level offline RL (value estimation and policy
extraction) from the offline training phase, forming a non-iterative bi-level paradigm and …

We propose VRL3, a powerful data-driven framework with a simple design for solving
challenging visual deep reinforcement learning (DRL) tasks. We analyze a number of major …