18 citations · 37 across the 4 of their papers we have counts for
4 papers
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…
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…
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…
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…