Offline reinforcement learning (RL) aims to learn optimal policies from offline datasets,
where the parameterization of policies is crucial but often overlooked. Recently, Diffsuion-QL …

Offline-to-online reinforcement learning (RL) is a training paradigm that combines pre-
training on a pre-collected dataset with fine-tuning in an online environment. However, the …

The divergence of the Q-value estimation has been a prominent issue offline reinforcement
learning (offline RL), where the agent has no access to real dynamics. Traditional beliefs …

Training practical agents usually involve offline and online reinforcement learning (RL) to
balance the policy's performance and interaction costs. In particular, online fine-tuning has …

Recent developments in offline reinforcement learning have uncovered the immense
potential of diffusion modeling, which excels at representing heterogeneous behavior …

This paper presents an exploration of preference learning in text-to-motion generation. We
find that current improvements in text-to-motion generation still rely on datasets requiring …

Behavioural cloning (BC) is a commonly used imitation learning method to infer a sequential
decision-making policy from expert demonstrations. However, when the quality of the data is …

In recent years, offline reinforcement learning (RL) algorithms have gained considerable
attention. However, the role of data sampling techniques in offline RL has been somewhat …

We consider the problem of federated offline reinforcement learning (RL), a scenario under
which distributed learning agents must collaboratively learn a high-quality control policy only …

One of the key challenges of Reinforcement Learning (RL) is the ability of agents to
generalise their learned policy to unseen settings. Moreover, training RL agents requires …