Offline reinforcement learning (RL) provides a promising solution to learning an agent fully
relying on a data-driven paradigm. However, constrained by the limited quality of the offline …

Deep reinforcement learning (RL) has emerged as a promising solution for autonomous
devices requiring sequential decision-making. In the online RL framework, the agent must …

Offline safe reinforcement learning (RL) aims to train a constraint satisfaction policy from a
fixed dataset. Current state-of-the-art approaches are based on supervised learning with a …

Debt collection is utilized for risk control after credit card delinquency. The existing rule-
based method tends to be myopic and non-adaptive due to the delayed feedback …

Abstract Offline Reinforcement Learning (Offline RL) is able to learn from pre-collected
offline data without real-time interaction with the environment by policy regularization via …

The accuracy of laser gas detection technology is influenced by the temperature of the
optical cavity. Traditional control methods suffer from inadequacies in fully considering the …

Offline reinforcement learning (RL) enables policy learning from pre-collected offline
datasets, relaxing the need to interact directly with the environment. However, limited by the …

Join query optimization aims to find the best join order for tables in a query, which is critical
for query processing performance. Recently, reinforcement learning models have been …

Decision Transformer (DT), a trajectory modeling method, has shown competitive
performance compared to traditional offline reinforcement learning (RL) approaches on …

Despite the superior performance of Large language models on many NLP tasks, they still
face significant limitations in memorizing extensive world knowledge. Recent studies have …