Search Results for author: Kun Shao

Found 16 papers, 5 papers with code

Distilling Morphology-Conditioned Hypernetworks for Efficient Universal Morphology Control

1 code implementation9 Feb 2024 Zheng Xiong, Risto Vuorio, Jacob Beck, Matthieu Zimmer, Kun Shao, Shimon Whiteson

Learning a universal policy across different robot morphologies can significantly improve learning efficiency and enable zero-shot generalization to unseen morphologies.

Zero-shot Generalization

A survey on algorithms for Nash equilibria in finite normal-form games

no code implementations18 Dec 2023 Hanyu Li, Wenhan Huang, Zhijian Duan, David Henry Mguni, Kun Shao, Jun Wang, Xiaotie Deng

This paper reviews various algorithms computing the Nash equilibrium and its approximation solutions in finite normal-form games from both theoretical and empirical perspectives.

ChessGPT: Bridging Policy Learning and Language Modeling

1 code implementation NeurIPS 2023 Xidong Feng, Yicheng Luo, Ziyan Wang, Hongrui Tang, Mengyue Yang, Kun Shao, David Mguni, Yali Du, Jun Wang

Thus, we propose ChessGPT, a GPT model bridging policy learning and language modeling by integrating data from these two sources in Chess games.

Decision Making Language Modelling

Traj-MAE: Masked Autoencoders for Trajectory Prediction

no code implementations ICCV 2023 Hao Chen, Jiaze Wang, Kun Shao, Furui Liu, Jianye Hao, Chenyong Guan, Guangyong Chen, Pheng-Ann Heng

Specifically, our Traj-MAE employs diverse masking strategies to pre-train the trajectory encoder and map encoder, allowing for the capture of social and temporal information among agents while leveraging the effect of environment from multiple granularities.

Autonomous Driving Trajectory Prediction

Timing is Everything: Learning to Act Selectively with Costly Actions and Budgetary Constraints

no code implementations31 May 2022 David Mguni, Aivar Sootla, Juliusz Ziomek, Oliver Slumbers, Zipeng Dai, Kun Shao, Jun Wang

In this paper, we introduce a reinforcement learning (RL) framework named \textbf{L}earnable \textbf{I}mpulse \textbf{C}ontrol \textbf{R}einforcement \textbf{A}lgorithm (LICRA), for learning to optimally select both when to act and which actions to take when actions incur costs.

Reinforcement Learning (RL)

Learning Explicit Credit Assignment for Multi-agent Joint Q-learning

no code implementations29 Sep 2021 Hangyu Mao, Jianye Hao, Dong Li, Jun Wang, Weixun Wang, Xiaotian Hao, Bin Wang, Kun Shao, Zhen Xiao, Wulong Liu

In contrast, we formulate an \emph{explicit} credit assignment problem where each agent gives its suggestion about how to weight individual Q-values to explicitly maximize the joint Q-value, besides guaranteeing the Bellman optimality of the joint Q-value.


Cooperative Multi-Agent Transfer Learning with Level-Adaptive Credit Assignment

no code implementations1 Jun 2021 Tianze Zhou, Fubiao Zhang, Kun Shao, Kai Li, Wenhan Huang, Jun Luo, Weixun Wang, Yaodong Yang, Hangyu Mao, Bin Wang, Dong Li, Wulong Liu, Jianye Hao

In addition, we use a novel agent network named Population Invariant agent with Transformer (PIT) to realize the coordination transfer in more varieties of scenarios.

Management Multi-agent Reinforcement Learning +3

Robust Multi-Agent Reinforcement Learning Driven by Correlated Equilibrium

no code implementations1 Jan 2021 Yizheng Hu, Kun Shao, Dong Li, Jianye Hao, Wulong Liu, Yaodong Yang, Jun Wang, Zhanxing Zhu

Therefore, to achieve robust CMARL, we introduce novel strategies to encourage agents to learn correlated equilibrium while maximally preserving the convenience of the decentralized execution.

Adversarial Robustness reinforcement-learning +2

Multi-Agent Determinantal Q-Learning

1 code implementation ICML 2020 Yaodong Yang, Ying Wen, Li-Heng Chen, Jun Wang, Kun Shao, David Mguni, Wei-Nan Zhang

Though practical, current methods rely on restrictive assumptions to decompose the centralized value function across agents for execution.


A Survey of Deep Reinforcement Learning in Video Games

no code implementations23 Dec 2019 Kun Shao, Zhentao Tang, Yuanheng Zhu, Nannan Li, Dongbin Zhao

In this paper, we survey the progress of DRL methods, including value-based, policy gradient, and model-based algorithms, and compare their main techniques and properties.

Real-Time Strategy Games reinforcement-learning +1

StarCraft Micromanagement with Reinforcement Learning and Curriculum Transfer Learning

1 code implementation3 Apr 2018 Kun Shao, Yuanheng Zhu, Dongbin Zhao

With reinforcement learning and curriculum transfer learning, our units are able to learn appropriate strategies in StarCraft micromanagement scenarios.

reinforcement-learning Reinforcement Learning (RL) +2

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