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Found 7 papers, 4 papers with code
Sequential Decision-Making for Inline Text Autocomplete
In particular, we hypothesize that the objectives under which sequential decision-making can improve autocomplete systems are not tailored solely to text entry speed, but more broadly to metrics such as user satisfaction and convenience.
A Dense Reward View on Aligning Text-to-Image Diffusion with Preference
Aligning text-to-image diffusion model (T2I) with preference has been gaining increasing research attention.
Fantastic Rewards and How to Tame Them: A Case Study on Reward Learning for Task-oriented Dialogue Systems
Prior works mainly focus on adopting advanced RL techniques to train the ToD agents, while the design of the reward function is not well studied.
A Unified Framework for Alternating Offline Model Training and Policy Learning
In offline model-based reinforcement learning (offline MBRL), we learn a dynamic model from historically collected data, and subsequently utilize the learned model and fixed datasets for policy learning, without further interacting with the environment.
Regularizing a Model-based Policy Stationary Distribution to Stabilize Offline Reinforcement Learning
Offline reinforcement learning (RL) extends the paradigm of classical RL algorithms to purely learning from static datasets, without interacting with the underlying environment during the learning process.
A Behavior Regularized Implicit Policy for Offline Reinforcement Learning
For training more effective agents, we propose a framework that supports learning a flexible yet well-regularized fully-implicit policy.
State-Action Joint Regularized Implicit Policy for Offline Reinforcement Learning
For training more effective agents, we propose a framework that supports learning a flexible and well-regularized policy, which consists of a fully implicit policy and a regularization through the state-action visitation frequency induced by the current policy and that induced by the data-collecting behavior policy.
