49 citations · 132 across the 14 of their papers we have counts for
14 papers
Never Explore Repeatedly in Multi-Agent Reinforcement Learning
Chenghao Li, Tonghan Wang, Chongjie Zhang +1
In the realm of multi-agent reinforcement learning, intrinsic motivations have emerged as a pivotal tool for exploration. While the computation of many intrinsic rewards relies on…
Learning Diverse Risk Preferences in Population-based Self-play
Yuhua Jiang, Qihan Liu, Xiaoteng Ma +5
Among the great successes of Reinforcement Learning (RL), self-play algorithms play an essential role in solving competitive games. Current self-play algorithms optimize the agent…
The Provable Benefits of Unsupervised Data Sharing for Offline Reinforcement Learning
Hao Hu, Yiqin Yang, Qianchuan Zhao +1
Self-supervised methods have become crucial for advancing deep learning by leveraging data itself to reduce the need for expensive annotations. However, the question of how to cond…
Flow to Control: Offline Reinforcement Learning with Lossless Primitive Discovery
Yiqin Yang, Hao Hu, Wenzhe Li +4
Offline reinforcement learning (RL) enables the agent to effectively learn from logged data, which significantly extends the applicability of RL algorithms in real-world scenarios…
Distributionally Robust Offline Reinforcement Learning with Linear Function Approximation
Xiaoteng Ma, Zhipeng Liang, Jose Blanchet +5
Among the reasons hindering reinforcement learning (RL) applications to real-world problems, two factors are critical: limited data and the mismatch between the testing environment…
On the Role of Discount Factor in Offline Reinforcement Learning
Hao Hu, Yiqin Yang, Qianchuan Zhao +1
Offline reinforcement learning (RL) enables effective learning from previously collected data without exploration, which shows great promise in real-world applications when explora…
Mean-Semivariance Policy Optimization via Risk-Averse Reinforcement Learning
Xiaoteng Ma, Shuai Ma, Li Xia +1
Keeping risk under control is often more crucial than maximizing expected rewards in real-world decision-making situations, such as finance, robotics, autonomous driving, etc. The…
Offline Reinforcement Learning with Value-based Episodic Memory
Xiaoteng Ma, Yiqin Yang, Hao Hu +5
Offline reinforcement learning (RL) shows promise of applying RL to real-world problems by effectively utilizing previously collected data. Most existing offline RL algorithms use…
MPSN: Motion-aware Pseudo Siamese Network for Indoor Video Head Detection in Buildings
Kailai Sun, Xiaoteng Ma, Peng Liu +1
Head detection in the indoor video is an essential component of building occupancy detection. While deep models have achieved remarkable progress in general object detection, they…
Average-Reward Reinforcement Learning with Trust Region Methods
Xiaoteng Ma, Xiaohang Tang, Li Xia +2
Most of reinforcement learning algorithms optimize the discounted criterion which is beneficial to accelerate the convergence and reduce the variance of estimates. Although the dis…
Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement Learning
Yiqin Yang, Xiaoteng Ma, Chenghao Li +5
Learning from datasets without interaction with environments (Offline Learning) is an essential step to apply Reinforcement Learning (RL) algorithms in real-world scenarios. Howeve…
Celebrating Diversity in Shared Multi-Agent Reinforcement Learning
Chenghao Li, Tonghan Wang, Chengjie Wu +3
Recently, deep multi-agent reinforcement learning (MARL) has shown the promise to solve complex cooperative tasks. Its success is partly because of parameter sharing among agents.…
Modeling the Interaction between Agents in Cooperative Multi-Agent Reinforcement Learning
Xiaoteng Ma, Yiqin Yang, Chenghao Li +3
Value-based methods of multi-agent reinforcement learning (MARL), especially the value decomposition methods, have been demonstrated on a range of challenging cooperative tasks. Ho…
SOAC: The Soft Option Actor-Critic Architecture
Chenghao Li, Xiaoteng Ma, Chongjie Zhang +3
The option framework has shown great promise by automatically extracting temporally-extended sub-tasks from a long-horizon task. Methods have been proposed for concurrently learnin…