Diffusion models have demonstrated highly-expressive generative capabilities in vision and
NLP. Recent studies in reinforcement learning (RL) have shown that diffusion models are …

A key theme in the past decade has been that when large neural networks and large
datasets combine they can produce remarkable results. In deep reinforcement learning (RL) …

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 …

Despite the success of Random Network Distillation (RND) in various domains, it was shown
as not discriminative enough to be used as an uncertainty estimator for penalizing out-of …

Recent years have witnessed significant advancements in offline reinforcement learning
(RL), resulting in the development of numerous algorithms with varying degrees of …

Offline reinforcement learning (RL) aims to find a near-optimal policy using pre-collected
datasets. In real-world scenarios, data collection could be costly and risky; therefore, offline …

Offline reinforcement learning (RL) presents a promising approach for learning reinforced
policies from offline datasets without the need for costly or unsafe interactions with the …

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

Decision Transformer (DT), which employs expressive sequence modeling techniques to
perform action generation, has emerged as a promising approach to offline policy …

As a data-driven paradigm, offline reinforcement learning (RL) has been formulated as
sequence modeling that conditions on the hindsight information including returns, goal or …