19 citations · 19 across the 2 of their papers we have counts for
4 papers
Model-based Lifelong Reinforcement Learning with Bayesian Exploration
Haotian Fu, Shangqun Yu, Michael Littman +1
We propose a model-based lifelong reinforcement-learning approach that estimates a hierarchical Bayesian posterior distilling the common structure shared across different tasks. Th…
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…
MGHRL: Meta Goal-generation for Hierarchical Reinforcement Learning
Haotian Fu, Hongyao Tang, Jianye Hao +2
Most meta reinforcement learning (meta-RL) methods learn to adapt to new tasks by directly optimizing the parameters of policies over primitive action space. Such algorithms work w…
Deep Multi-Agent Reinforcement Learning with Discrete-Continuous Hybrid Action Spaces
Haotian Fu, Hongyao Tang, Jianye Hao +3
Deep Reinforcement Learning (DRL) has been applied to address a variety of cooperative multi-agent problems with either discrete action spaces or continuous action spaces. However,…