8 citations · 21 across the 10 of their papers we have counts for
11 papers
State-Aware Proximal Pessimistic Algorithms for Offline Reinforcement Learning
Chen Chen, Hongyao Tang, Yi Ma +4
Pessimism is of great importance in offline reinforcement learning (RL). One broad category of offline RL algorithms fulfills pessimism by explicit or implicit behavior regularizat…
Prototypical context-aware dynamics generalization for high-dimensional model-based reinforcement learning
Junjie Wang, Yao Mu, Dong Li +6
The latent world model provides a promising way to learn policies in a compact latent space for tasks with high-dimensional observations, however, its generalization across diverse…
Decomposed Mutual Information Optimization for Generalized Context in Meta-Reinforcement Learning
Yao Mu, Yuzheng Zhuang, Fei Ni +4
Adapting to the changes in transition dynamics is essential in robotic applications. By learning a conditional policy with a compact context, context-aware meta-reinforcement learn…
On the Convergence Theory of Meta Reinforcement Learning with Personalized Policies
Haozhi Wang, Qing Wang, Yunfeng Shao +3
Modern meta-reinforcement learning (Meta-RL) methods are mainly developed based on model-agnostic meta-learning, which performs policy gradient steps across tasks to maximize polic…
Towards A Unified Policy Abstraction Theory and Representation Learning Approach in Markov Decision Processes
Min Zhang, Hongyao Tang, Jianye Hao +1
Lying on the heart of intelligent decision-making systems, how policy is represented and optimized is a fundamental problem. The root challenge in this problem is the large scale a…
PAnDR: Fast Adaptation to New Environments from Offline Experiences via Decoupling Policy and Environment Representations
Tong Sang, Hongyao Tang, Yi Ma +5
Deep Reinforcement Learning (DRL) has been a promising solution to many complex decision-making problems. Nevertheless, the notorious weakness in generalization among environments…