183 citations · 444 across the 21 of their papers we have counts for
21 papers
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…
Learning Multi-Agent Intention-Aware Communication for Optimal Multi-Order Execution in Finance
Yuchen Fang, Zhenggang Tang, Kan Ren +7
Order execution is a fundamental task in quantitative finance, aiming at finishing acquisition or liquidation for a number of trading orders of the specific assets. Recent advance…
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…
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…
H-TSP: Hierarchically Solving the Large-Scale Travelling Salesman Problem
Xuanhao Pan, Yan Jin, Yuandong Ding +4
We propose an end-to-end learning framework based on hierarchical reinforcement learning, called H-TSP, for addressing the large-scale Travelling Salesman Problem (TSP). The propos…
An Adaptive Deep RL Method for Non-Stationary Environments with Piecewise Stable Context
Xiaoyu Chen, Xiangming Zhu, Yufeng Zheng +8
One of the key challenges in deploying RL to real-world applications is to adapt to variations of unknown environment contexts, such as changing terrains in robotic tasks and fluct…
Multi-Agent Reinforcement Learning with Shared Resources for Inventory Management
Yuandong Ding, Mingxiao Feng, Guozi Liu +7
In this paper, we consider the inventory management (IM) problem where we need to make replenishment decisions for a large number of stock keeping units (SKUs) to balance their sup…
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…
Inspector: Pixel-Based Automated Game Testing via Exploration, Detection, and Investigation
Guoqing Liu, Mengzhang Cai, Li Zhao +4
Deep reinforcement learning (DRL) has attracted much attention in automated game testing. Early attempts rely on game internal information for game space exploration, thus requirin…
Tiered Reinforcement Learning: Pessimism in the Face of Uncertainty and Constant Regret
Jiawei Huang, Li Zhao, Tao Qin +3
We propose a new learning framework that captures the tiered structure of many real-world user-interaction applications, where the users can be divided into two groups based on the…
Towards Deployment-Efficient Reinforcement Learning: Lower Bound and Optimality
Jiawei Huang, Jinglin Chen, Li Zhao +3
Deployment efficiency is an important criterion for many real-world applications of reinforcement learning (RL). Despite the community's increasing interest, there lacks a formal t…
Curriculum Offline Imitation Learning
Minghuan Liu, Hanye Zhao, Zhengyu Yang +4
Offline reinforcement learning (RL) tasks require the agent to learn from a pre-collected dataset with no further interactions with the environment. Despite the potential to surpas…
Object-Aware Regularization for Addressing Causal Confusion in Imitation Learning
Jongjin Park, Younggyo Seo, Chang Liu +4
Behavioral cloning has proven to be effective for learning sequential decision-making policies from expert demonstrations. However, behavioral cloning often suffers from the causal…
Distributional Reinforcement Learning for Multi-Dimensional Reward Functions
Pushi Zhang, Xiaoyu Chen, Li Zhao +3
A growing trend for value-based reinforcement learning (RL) algorithms is to capture more information than scalar value functions in the value network. One of the most well-known m…
Return-Based Contrastive Representation Learning for Reinforcement Learning
Guoqing Liu, Chuheng Zhang, Li Zhao +5
Recently, various auxiliary tasks have been proposed to accelerate representation learning and improve sample efficiency in deep reinforcement learning (RL). However, existing auxi…
Suphx: Mastering Mahjong with Deep Reinforcement Learning
Junjie Li, Sotetsu Koyamada, Qiwei Ye +7
Artificial Intelligence (AI) has achieved great success in many domains, and game AI is widely regarded as its beachhead since the dawn of AI. In recent years, studies on game AI h…
Trimming the Sail: A Second-order Learning Paradigm for Stock Prediction
Chi Chen, Li Zhao, Wei Cao +2
Nowadays, machine learning methods have been widely used in stock prediction. Traditional approaches assume an identical data distribution, under which a learned model on the train…
Distributional Reward Decomposition for Reinforcement Learning
Zichuan Lin, Li Zhao, Derek Yang +3
Many reinforcement learning (RL) tasks have specific properties that can be leveraged to modify existing RL algorithms to adapt to those tasks and further improve performance, and…
Fully Parameterized Quantile Function for Distributional Reinforcement Learning
Derek Yang, Li Zhao, Zichuan Lin +3
Distributional Reinforcement Learning (RL) differs from traditional RL in that, rather than the expectation of total returns, it estimates distributions and has achieved state-of-t…
Reinforcement Learning for Relation Classification from Noisy Data
Jun Feng, Minlie Huang, Li Zhao +2
Existing relation classification methods that rely on distant supervision assume that a bag of sentences mentioning an entity pair are all describing a relation for the entity pair…
Adversarial Neural Machine Translation
Lijun Wu, Yingce Xia, Li Zhao +4
In this paper, we study a new learning paradigm for Neural Machine Translation (NMT). Instead of maximizing the likelihood of the human translation as in previous works, we minimiz…