Effective offline RL methods require properly handling out-of-distribution actions. Implicit Q-
learning (IQL) addresses this by training a Q-function using only dataset actions through a …

Unsupervised pre-training has recently become the bedrock for computer vision and natural
language processing. In reinforcement learning (RL), goal-conditioned RL can potentially …

Reward functions are difficult to design and often hard to align with human intent. Preference-
based Reinforcement Learning (RL) algorithms address these problems by learning reward …

Reinforcement Learning from Human Feedback (RLHF) has emerged as a popular
paradigm for aligning models with human intent. Typically RLHF algorithms operate in two …

In reinforcement learning (RL), representation learning is a proven tool for complex image-
based tasks, but is often overlooked for environments with low-level states, such as physical …

Abstract We show that Deep Neural Networks introduce two heteroscedastic Gumbel noise
sources into Q-Learning. To account for these noise sources, we propose Double Gumbel Q …

Abstract Generative Flow Networks (GFNs) have emerged as a powerful tool for sampling
discrete objects from unnormalized distributions, offering a scalable alternative to Markov …

Score-based generative models like the diffusion model have been testified to be effective in
modeling multi-modal data from image generation to reinforcement learning (RL). However …

Combining offline and online reinforcement learning (RL) is crucial for efficient and safe
learning. However, previous approaches treat offline and online learning as separate …

Offline-to-online reinforcement learning (RL), by combining the benefits of offline pretraining
and online finetuning, promises enhanced sample efficiency and policy performance …