Search Results for author: Rundong Wang

Found 16 papers, 0 papers with code

Attention over Self-attention:Intention-aware Re-ranking with Dynamic Transformer Encoders for Recommendation

no code implementations14 Jan 2022 Zhuoyi Lin, Sheng Zang, Rundong Wang, Zhu Sun, J. Senthilnath, Chi Xu, Chee-Keong Kwoh

We then introduce a dynamic transformer encoder (DTE) to capture user-specific inter-item relationships among item candidates by seamlessly accommodating the learned latent user intentions via IDM.

Re-Ranking

DeepScalper: A Risk-Aware Deep Reinforcement Learning Framework for Intraday Trading with Micro-level Market Embedding

no code implementations15 Dec 2021 Shuo Sun, Rundong Wang, Xu He, Junlei Zhu, Jian Li, Bo An

However, it is hard to apply existing RL methods to intraday trading due to the following three limitations: 1) overlooking micro-level market information (e. g., limit order book); 2) only focusing on local price fluctuation and failing to capture the overall trend of the whole trading day; 3) neglecting the impact of market risk.

Algorithmic Trading Management +1

RMIX: Learning Risk-Sensitive Policies forCooperative Reinforcement Learning Agents

no code implementations NeurIPS 2021 Wei Qiu, Xinrun Wang, Runsheng Yu, Rundong Wang, Xu He, Bo An, Svetlana Obraztsova, Zinovi Rabinovich

Current value-based multi-agent reinforcement learning methods optimize individual Q values to guide individuals' behaviours via centralized training with decentralized execution (CTDE).

Multi-agent Reinforcement Learning reinforcement-learning +2

Reinforcement Learning for Quantitative Trading

no code implementations28 Sep 2021 Shuo Sun, Rundong Wang, Bo An

RL's impact is pervasive, recently demonstrating its ability to conquer many challenging QT tasks.

Decision Making reinforcement-learning

RMIX: Learning Risk-Sensitive Policies for Cooperative Reinforcement Learning Agents

no code implementations16 Feb 2021 Wei Qiu, Xinrun Wang, Runsheng Yu, Xu He, Rundong Wang, Bo An, Svetlana Obraztsova, Zinovi Rabinovich

Current value-based multi-agent reinforcement learning methods optimize individual Q values to guide individuals' behaviours via centralized training with decentralized execution (CTDE).

Multi-agent Reinforcement Learning reinforcement-learning +2

Safe Coupled Deep Q-Learning for Recommendation Systems

no code implementations8 Jan 2021 Runsheng Yu, Yu Gong, Rundong Wang, Bo An, Qingwen Liu, Wenwu Ou

Firstly, we introduce a novel training scheme with two value functions to maximize the accumulated long-term reward under the safety constraint.

Q-Learning Recommendation Systems

RMIX: Risk-Sensitive Multi-Agent Reinforcement Learning

no code implementations1 Jan 2021 Wei Qiu, Xinrun Wang, Runsheng Yu, Xu He, Rundong Wang, Bo An, Svetlana Obraztsova, Zinovi Rabinovich

Centralized training with decentralized execution (CTDE) has become an important paradigm in multi-agent reinforcement learning (MARL).

Multi-agent Reinforcement Learning reinforcement-learning +2

Deep Stock Trading: A Hierarchical Reinforcement Learning Framework for Portfolio Optimization and Order Execution

no code implementations23 Dec 2020 Rundong Wang, Hongxin Wei, Bo An, Zhouyan Feng, Jun Yao

Portfolio management via reinforcement learning is at the forefront of fintech research, which explores how to optimally reallocate a fund into different financial assets over the long term by trial-and-error.

Hierarchical Reinforcement Learning Management +2

MetaInfoNet: Learning Task-Guided Information for Sample Reweighting

no code implementations9 Dec 2020 Hongxin Wei, Lei Feng, Rundong Wang, Bo An

Deep neural networks have been shown to easily overfit to biased training data with label noise or class imbalance.

Meta-Learning

Learning Expensive Coordination: An Event-Based Deep RL Approach

no code implementations ICLR 2020 Zhenyu Shi*, Runsheng Yu*, Xinrun Wang*, Rundong Wang, Youzhi Zhang, Hanjiang Lai, Bo An

The main difficulties of expensive coordination are that i) the leader has to consider the long-term effect and predict the followers' behaviors when assigning bonuses and ii) the complex interactions between followers make the training process hard to converge, especially when the leader's policy changes with time.

Decision Making Multi-agent Reinforcement Learning

Inducing Cooperation via Team Regret Minimization based Multi-Agent Deep Reinforcement Learning

no code implementations18 Nov 2019 Runsheng Yu, Zhenyu Shi, Xinrun Wang, Rundong Wang, Buhong Liu, Xinwen Hou, Hanjiang Lai, Bo An

Existing value-factorized based Multi-Agent deep Reinforce-ment Learning (MARL) approaches are well-performing invarious multi-agent cooperative environment under thecen-tralized training and decentralized execution(CTDE) scheme, where all agents are trained together by the centralized valuenetwork and each agent execute its policy independently.

reinforcement-learning

Cannot find the paper you are looking for? You can Submit a new open access paper.