151 citations · 678 across the 50 of their papers we have counts for
8 papers · 1 filter
Symmetry-Aware Robot Design with Structured Subgroups
Heng Dong, Junyu Zhang, Tonghan Wang +1
Robot design aims at learning to create robots that can be easily controlled and perform tasks efficiently. Previous works on robot design have proven its ability to generate robot…
CUP: Critic-Guided Policy Reuse
Jin Zhang, Siyuan Li, Chongjie Zhang
The ability to reuse previous policies is an important aspect of human intelligence. To achieve efficient policy reuse, a Deep Reinforcement Learning (DRL) agent needs to decide wh…
Multi-Agent Policy Transfer via Task Relationship Modeling
Rongjun Qin, Feng Chen, Tonghan Wang +5
Team adaptation to new cooperative tasks is a hallmark of human intelligence, which has yet to be fully realized in learning agents. Previous work on multi-agent transfer learning…
Fever Basketball: A Complex, Flexible, and Asynchronized Sports Game Environment for Multi-agent Reinforcement Learning
Hangtian Jia, Yujing Hu, Yingfeng Chen +4
The development of deep reinforcement learning (DRL) has benefited from the emergency of a variety type of game environments where new challenging problems are proposed and new alg…
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…
MetaCURE: Meta Reinforcement Learning with Empowerment-Driven Exploration
Jin Zhang, Jianhao Wang, Hao Hu +4
Meta reinforcement learning (meta-RL) extracts knowledge from previous tasks and achieves fast adaptation to new tasks. Despite recent progress, efficient exploration in meta-RL re…
Context-Aware Policy Reuse
Siyuan Li, Fangda Gu, Guangxiang Zhu +1
Transfer learning can greatly speed up reinforcement learning for a new task by leveraging policies of relevant tasks. Existing works of policy reuse either focus on only selecting…
An Optimal Online Method of Selecting Source Policies for Reinforcement Learning
Siyuan Li, Chongjie Zhang
Transfer learning significantly accelerates the reinforcement learning process by exploiting relevant knowledge from previous experiences. The problem of optimally selecting source…