8 citations · 16 across the 6 of their papers we have counts for
6 papers
Hard Prompts Made Interpretable: Sparse Entropy Regularization for Prompt Tuning with RL
Yunseon Choi, Sangmin Bae, Seonghyun Ban +6
With the advent of foundation models, prompt tuning has positioned itself as an important technique for directing model behaviors and eliciting desired responses. Prompt tuning reg…
Pre-Trained Large Language Models for Industrial Control
Lei Song, Chuheng Zhang, Li Zhao +1
For industrial control, developing high-performance controllers with few samples and low technical debt is appealing. Foundation models, possessing rich prior knowledge obtained fr…
A Versatile Multi-Agent Reinforcement Learning Benchmark for Inventory Management
Xianliang Yang, Zhihao Liu, Wei Jiang +4
Multi-agent reinforcement learning (MARL) models multiple agents that interact and learn within a shared environment. This paradigm is applicable to various industrial scenarios su…
Mildly Constrained Evaluation Policy for Offline Reinforcement Learning
Linjie Xu, Zhengyao Jiang, Jinyu Wang +2
Offline reinforcement learning (RL) methodologies enforce constraints on the policy to adhere closely to the behavior policy, thereby stabilizing value learning and mitigating the…
Pointerformer: Deep Reinforced Multi-Pointer Transformer for the Traveling Salesman Problem
Yan Jin, Yuandong Ding, Xuanhao Pan +5
Traveling Salesman Problem (TSP), as a classic routing optimization problem originally arising in the domain of transportation and logistics, has become a critical task in broader…
TD3 with Reverse KL Regularizer for Offline Reinforcement Learning from Mixed Datasets
Yuanying Cai, Chuheng Zhang, Li Zhao +6
We consider an offline reinforcement learning (RL) setting where the agent need to learn from a dataset collected by rolling out multiple behavior policies. There are two challenge…