30 citations · 73 across the 16 of their papers we have counts for
16 papers
Learning Stateful Predictive Knowledge From Experience
Yan Song, Xidong Feng, Bo Liu +7
As large language model (LLM) agents increasingly learn from experience, they primarily rely on trajectory-level reflection to extract insights. Viewed through the lens of predicti…
Natural Language Reinforcement Learning
Xidong Feng, Bo Liu, Yan Song +7
Artificial intelligence progresses towards the "Era of Experience," where agents are expected to learn from continuous, grounded interaction. We argue that traditional Reinforcemen…
Efficient Reinforcement Learning with Large Language Model Priors
Xue Yan, Yan Song, Xidong Feng +4
In sequential decision-making (SDM) tasks, methods like reinforcement learning (RL) and heuristic search have made notable advances in specific cases. However, they often require e…
Natural Language Reinforcement Learning
Xidong Feng, Ziyu Wan, Mengyue Yang +5
Reinforcement Learning (RL) has shown remarkable abilities in learning policies for decision-making tasks. However, RL is often hindered by issues such as low sample efficiency, la…
Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning
Filippos Christianos, Georgios Papoudakis, Matthieu Zimmer +13
A key method for creating Artificial Intelligence (AI) agents is Reinforcement Learning (RL). However, constructing a standalone RL policy that maps perception to action directly e…
Alphazero-like Tree-Search can Guide Large Language Model Decoding and Training
Xidong Feng, Ziyu Wan, Muning Wen +4
Recent works like Tree-of-Thought (ToT) and Reasoning via Planning (RAP) aim to augment the reasoning capabilities of LLMs by using tree-search algorithms to guide multi-step reaso…
ChessGPT: Bridging Policy Learning and Language Modeling
Xidong Feng, Yicheng Luo, Ziyan Wang +6
When solving decision-making tasks, humans typically depend on information from two key sources: (1) Historical policy data, which provides interaction replay from the environment,…
MANSA: Learning Fast and Slow in Multi-Agent Systems
David Mguni, Haojun Chen, Taher Jafferjee +7
In multi-agent reinforcement learning (MARL), independent learning (IL) often shows remarkable performance and easily scales with the number of agents. Yet, using IL can be ineffic…
Contextual Transformer for Offline Meta Reinforcement Learning
Runji Lin, Ye Li, Xidong Feng +6
The pretrain-finetuning paradigm in large-scale sequence models has made significant progress in natural language processing and computer vision tasks. However, such a paradigm is…
TorchOpt: An Efficient Library for Differentiable Optimization
Jie Ren, Xidong Feng, Bo Liu +4
Recent years have witnessed the booming of various differentiable optimization algorithms. These algorithms exhibit different execution patterns, and their execution needs massive…
Heterogeneous-Agent Mirror Learning: A Continuum of Solutions to Cooperative MARL
Jakub Grudzien Kuba, Xidong Feng, Shiyao Ding +3
The necessity for cooperation among intelligent machines has popularised cooperative multi-agent reinforcement learning (MARL) in the artificial intelligence (AI) research communit…
Towards Human-Level Bimanual Dexterous Manipulation with Reinforcement Learning
Yuanpei Chen, Tianhao Wu, Shengjie Wang +8
Achieving human-level dexterity is an important open problem in robotics. However, tasks of dexterous hand manipulation, even at the baby level, are challenging to solve through re…
A Theoretical Understanding of Gradient Bias in Meta-Reinforcement Learning
Xidong Feng, Bo Liu, Jie Ren +5
Gradient-based Meta-RL (GMRL) refers to methods that maintain two-level optimisation procedures wherein the outer-loop meta-learner guides the inner-loop gradient-based reinforceme…
CMML: Contextual Modulation Meta Learning for Cold-Start Recommendation
Xidong Feng, Chen Chen, Dong Li +3
Practical recommender systems experience a cold-start problem when observed user-item interactions in the history are insufficient. Meta learning, especially gradient based one, ca…
Neural Auto-Curricula
Xidong Feng, Oliver Slumbers, Ziyu Wan +5
When solving two-player zero-sum games, multi-agent reinforcement learning (MARL) algorithms often create populations of agents where, at each iteration, a new agent is discovered…
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…