18 citations · 96 across the 23 of their papers we have counts for
3 papers · 1 filter
What About Inputing Policy in Value Function: Policy Representation and Policy-extended Value Function Approximator
Hongyao Tang, Zhaopeng Meng, Jianye Hao +9
We study Policy-extended Value Function Approximator (PeVFA) in Reinforcement Learning (RL), which extends conventional value function approximator (VFA) to take as input not only…
Towards Effective Context for Meta-Reinforcement Learning: an Approach based on Contrastive Learning
Haotian Fu, Hongyao Tang, Jianye Hao +4
Context, the embedding of previous collected trajectories, is a powerful construct for Meta-Reinforcement Learning (Meta-RL) algorithms. By conditioning on an effective context, Me…
An Efficient Transfer Learning Framework for Multiagent Reinforcement Learning
Tianpei Yang, Weixun Wang, Hongyao Tang +9
Transfer Learning has shown great potential to enhance single-agent Reinforcement Learning (RL) efficiency. Similarly, Multiagent RL (MARL) can also be accelerated if agents can sh…