9 citations · 25 across the 9 of their papers we have counts for
10 papers
Stop Wandering, Find the Keys: LLMs Discriminate Key States for Efficient Multi-Agent Exploration
Yun Qu, Boyuan Wang, Yuhang Jiang +7
With expansive state-action spaces, efficient multi-agent exploration remains a longstanding challenge in reinforcement learning. Although pursuing novelty, diversity, or uncertain…
Hokoff: Real Game Dataset from Honor of Kings and its Offline Reinforcement Learning Benchmarks
Yun Qu, Boyuan Wang, Jianzhun Shao +15
The advancement of Offline Reinforcement Learning (RL) and Offline Multi-Agent Reinforcement Learning (MARL) critically depends on the availability of high-quality, pre-collected o…
LLM-Empowered State Representation for Reinforcement Learning
Boyuan Wang, Yun Qu, Yuhang Jiang +4
Conventional state representations in reinforcement learning often omit critical task-related details, presenting a significant challenge for value networks in establishing accurat…
Counterfactual Conservative Q Learning for Offline Multi-agent Reinforcement Learning
Jianzhun Shao, Yun Qu, Chen Chen +2
Offline multi-agent reinforcement learning is challenging due to the coupling effect of both distribution shift issue common in offline setting and the high dimension issue common…
Wasserstein Unsupervised Reinforcement Learning
Shuncheng He, Yuhang Jiang, Hongchang Zhang +2
Unsupervised reinforcement learning aims to train agents to learn a handful of policies or skills in environments without external reward. These pre-trained policies can accelerate…
Reducing Conservativeness Oriented Offline Reinforcement Learning
Hongchang Zhang, Jianzhun Shao, Yuhang Jiang +2
In offline reinforcement learning, a policy learns to maximize cumulative rewards with a fixed collection of data. Towards conservative strategy, current methods choose to regulari…
Credit Assignment with Meta-Policy Gradient for Multi-Agent Reinforcement Learning
Jianzhun Shao, Hongchang Zhang, Yuhang Jiang +2
Reward decomposition is a critical problem in centralized training with decentralized execution~(CTDE) paradigm for multi-agent reinforcement learning. To take full advantage of gl…
PFRL: Pose-Free Reinforcement Learning for 6D Pose Estimation
Jianzhun Shao, Yuhang Jiang, Gu Wang +2
6D pose estimation from a single RGB image is a challenging and vital task in computer vision. The current mainstream deep model methods resort to 2D images annotated with real-wor…
Skill Discovery of Coordination in Multi-agent Reinforcement Learning
Shuncheng He, Jianzhun Shao, Xiangyang Ji
Unsupervised skill discovery drives intelligent agents to explore the unknown environment without task-specific reward signal, and the agents acquire various skills which may be us…
Self6D: Self-Supervised Monocular 6D Object Pose Estimation
Gu Wang, Fabian Manhardt, Jianzhun Shao +3
6D object pose estimation is a fundamental problem in computer vision. Convolutional Neural Networks (CNNs) have recently proven to be capable of predicting reliable 6D pose estima…