1 code implementation • 19 Apr 2024 • Pengdeng Li, Shuxin Li, Xinrun Wang, Jakub Cerny, Youzhi Zhang, Stephen Mcaleer, Hau Chan, Bo An
Pursuit-evasion games (PEGs) model interactions between a team of pursuers and an evader in graph-based environments such as urban street networks.
no code implementations • 17 Apr 2024 • Pengdeng Li, Shuxin Li, Chang Yang, Xinrun Wang, Xiao Huang, Hau Chan, Bo An
(2) We propose the self-adaptive PSRO (SPSRO) by casting the hyperparameter value selection of the parametric PSRO as a hyperparameter optimization (HPO) problem where our objective is to learn an HPO policy that can self-adaptively determine the optimal hyperparameter values during the running of the parametric PSRO.
no code implementations • 26 Mar 2024 • Longtao Zheng, Zhiyuan Huang, Zhenghai Xue, Xinrun Wang, Bo An, Shuicheng Yan
We have open-sourced the environments, datasets, benchmarks, and interfaces to promote research towards developing general virtual agents for the future.
2 code implementations • 5 Mar 2024 • Weihao Tan, Ziluo Ding, Wentao Zhang, Boyu Li, Bohan Zhou, Junpeng Yue, Haochong Xia, Jiechuan Jiang, Longtao Zheng, Xinrun Xu, Yifei Bi, Pengjie Gu, Xinrun Wang, Börje F. Karlsson, Bo An, Zongqing Lu
Despite the success in specific tasks and scenarios, existing foundation agents, empowered by large models (LMs) and advanced tools, still cannot generalize to different scenarios, mainly due to dramatic differences in the observations and actions across scenarios.
no code implementations • 28 Feb 2024 • Wentao Zhang, Lingxuan Zhao, Haochong Xia, Shuo Sun, Jiaze Sun, Molei Qin, Xinyi Li, Yuqing Zhao, Yilei Zhao, Xinyu Cai, Longtao Zheng, Xinrun Wang, Bo An
Notably, FinAgent is the first advanced multimodal foundation agent designed for financial trading tasks.
1 code implementation • 25 Jan 2024 • Weihao Tan, Wentao Zhang, Shanqi Liu, Longtao Zheng, Xinrun Wang, Bo An
Despite the impressive performance across numerous tasks, large language models (LLMs) often fail in solving simple decision-making tasks due to the misalignment of the knowledge in LLMs with environments.
no code implementations • 31 Dec 2023 • Chaojie Wang, Yishi Xu, Zhong Peng, Chenxi Zhang, Bo Chen, Xinrun Wang, Lei Feng, Bo An
Large language models (LLMs) have exhibited remarkable performance on various natural language processing (NLP) tasks, especially for question answering.
1 code implementation • 17 Nov 2023 • Wentao Zhang, Yilei Zhao, Shuo Sun, Jie Ying, Yonggang Xie, Zitao Song, Xinrun Wang, Bo An
Specifically, the target stock pool of different investors varies dramatically due to their discrepancy on market states and individual investors may temporally adjust stocks they desire to trade (e. g., adding one popular stocks), which lead to customizable stock pools (CSPs).
1 code implementation • 22 Sep 2023 • Molei Qin, Shuo Sun, Wentao Zhang, Haochong Xia, Xinrun Wang, Bo An
In stage II, we construct a pool of diverse RL agents for different market trends, distinguished by return rates, where hundreds of RL agents are trained with different preferences of return rates and only a tiny fraction of them will be selected into the pool based on their profitability.
no code implementations • 14 Sep 2023 • Haochong Xia, Shuo Sun, Xinrun Wang, Bo An
Financial simulators play an important role in enhancing forecasting accuracy, managing risks, and fostering strategic financial decision-making.
1 code implementation • 13 Jun 2023 • Longtao Zheng, Rundong Wang, Xinrun Wang, Bo An
To address these challenges, we introduce Synapse, a computer agent featuring three key components: i) state abstraction, which filters out task-irrelevant information from raw states, allowing more exemplars within the limited context, ii) trajectory-as-exemplar prompting, which prompts the LLM with complete trajectories of the abstracted states and actions to improve multi-step decision-making, and iii) exemplar memory, which stores the embeddings of exemplars and retrieves them via similarity search for generalization to novel tasks.
no code implementations • 7 Feb 2023 • Pengdeng Li, Xinrun Wang, Shuxin Li, Hau Chan, Bo An
In this work, we attempt to bridge the two fields of finite-agent and infinite-agent games, by studying how the optimal policies of agents evolve with the number of agents (population size) in mean-field games, an agent-centric perspective in contrast to the existing works focusing typically on the convergence of the empirical distribution of the population.
no code implementations • 14 Jan 2023 • Shuo Sun, Molei Qin, Xinrun Wang, Bo An
Specifically, i) we propose AlphaMix+ as a strong FinRL baseline, which leverages mixture-of-experts (MoE) and risk-sensitive approaches to make diversified risk-aware investment decisions, ii) we evaluate 8 FinRL methods in 4 long-term real-world datasets of influential financial markets to demonstrate the usage of our PRUDEX-Compass, iii) PRUDEX-Compass together with 4 real-world datasets, standard implementation of 8 FinRL methods and a portfolio management environment is released as public resources to facilitate the design and comparison of new FinRL methods.
no code implementations • 7 Aug 2022 • Chang Yang, Ruiyu Wang, Xinrun Wang, Zhen Wang
However, there is not a unified formulation of the various schemes, as well as the comprehensive comparisons of methods across different schemes.
1 code implementation • 12 Jul 2022 • Shuxin Li, Xinrun Wang, Youzhi Zhang, Jakub Cerny, Pengdeng Li, Hau Chan, Bo An
Extensive experimental results demonstrate the superiority of our approach over offline RL algorithms and the importance of using model-based methods for OEF problems.
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 +3
no code implementations • 29 Sep 2021 • Runsheng Yu, Xinrun Wang, James Kwok
Most advanced Actor-Critic (AC) approaches update the actor and critic concurrently through (stochastic) Gradient Descents (GD), which may be trapped into bad local optimality due to the instability of these simultaneous updating schemes.
no code implementations • 2 Jun 2021 • Wanqi Xue, Youzhi Zhang, Shuxin Li, Xinrun Wang, Bo An, Chai Kiat Yeo
Securing networked infrastructures is important in the real world.
no code implementations • 18 May 2021 • Shuxin Li, Youzhi Zhang, Xinrun Wang, Wanqi Xue, Bo An
The challenge of solving this type of game is that the team's joint action space grows exponentially with the number of agents, which results in the inefficiency of the existing algorithms, e. g., Counterfactual Regret Minimization (CFR).
no code implementations • CVPR 2022 • Aye Phyu Phyu Aung, Xinrun Wang, Runsheng Yu, Bo An, Senthilnath Jayavelu, XiaoLi Li
In this paper, we propose a new approach to train Generative Adversarial Networks (GANs) where we deploy a double-oracle framework using the generator and discriminator oracles.
no code implementations • 16 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 +3
no code implementations • 1 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 +3
no code implementations • 7 Dec 2020 • Xinrun Wang, Tarun Nair, Haoyang Li, Yuh Sheng Reuben Wong, Nachiket Kelkar, Srinivas Vaidyanathan, Rajat Nayak, Bo An, Jagdish Krishnaswamy, Milind Tambe
Dams impact downstream river dynamics through flow regulation and disruption of upstream-downstream linkages.
no code implementations • 21 Aug 2020 • Xu He, Bo An, Yanghua Li, Haikai Chen, Rundong Wang, Xinrun Wang, Runsheng Yu, Xin Li, Zhirong Wang
Thus, the global policy of the whole page could be sub-optimal.
Multi-agent Reinforcement Learning Reinforcement Learning (RL)
no code implementations • 18 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.