63 citations · 212 across the 18 of their papers we have counts for
18 papers
Beyond the Dirac Delta: Mitigating Diversity Collapse in Reinforcement Fine-Tuning for Versatile Image Generation
Jinmei Liu, Haoru Li, Zhenhong Sun +6
Reinforcement learning (RL) has emerged as a powerful paradigm for fine-tuning large-scale generative models, such as diffusion and flow models, to align with complex human prefere…
Scalable In-Context Q-Learning
Jinmei Liu, Fuhong Liu, Zhenhong Sun +6
Recent advancements in language models have demonstrated remarkable in-context learning abilities, prompting the exploration of in-context reinforcement learning (ICRL) to extend t…
Meta-DT: Offline Meta-RL as Conditional Sequence Modeling with World Model Disentanglement
Zhi Wang, Li Zhang, Wenhao Wu +3
A longstanding goal of artificial general intelligence is highly capable generalists that can learn from diverse experiences and generalize to unseen tasks. The language and vision…
Discretizing Continuous Action Space with Unimodal Probability Distributions for On-Policy Reinforcement Learning
Yuanyang Zhu, Zhi Wang, Yuanheng Zhu +2
For on-policy reinforcement learning, discretizing action space for continuous control can easily express multiple modes and is straightforward to optimize. However, without consid…
Continual Offline Reinforcement Learning via Diffusion-based Dual Generative Replay
Jinmei Liu, Wenbin Li, Xiangyu Yue +3
We study continual offline reinforcement learning, a practical paradigm that facilitates forward transfer and mitigates catastrophic forgetting to tackle sequential offline tasks.…
Attention-Guided Contrastive Role Representations for Multi-Agent Reinforcement Learning
Zican Hu, Zongzhang Zhang, Huaxiong Li +3
Real-world multi-agent tasks usually involve dynamic team composition with the emergence of roles, which should also be a key to efficient cooperation in multi-agent reinforcement…
BiERL: A Meta Evolutionary Reinforcement Learning Framework via Bilevel Optimization
Junyi Wang, Yuanyang Zhu, Zhi Wang +3
Evolutionary reinforcement learning (ERL) algorithms recently raise attention in tackling complex reinforcement learning (RL) problems due to high parallelism, while they are prone…
Magnetic Field-Based Reward Shaping for Goal-Conditioned Reinforcement Learning
Hongyu Ding, Yuanze Tang, Qing Wu +3
Goal-conditioned reinforcement learning (RL) is an interesting extension of the traditional RL framework, where the dynamic environment and reward sparsity can cause conventional l…
Boosting Value Decomposition via Unit-Wise Attentive State Representation for Cooperative Multi-Agent Reinforcement Learning
Qingpeng Zhao, Yuanyang Zhu, Zichuan Liu +2
In cooperative multi-agent reinforcement learning (MARL), the environmental stochasticity and uncertainties will increase exponentially when the number of agents increases, which p…
MIXRTs: Toward Interpretable Multi-Agent Reinforcement Learning via Mixing Recurrent Soft Decision Trees
Zichuan Liu, Yuanyang Zhu, Zhi Wang +2
While achieving tremendous success in various fields, existing multi-agent reinforcement learning (MARL) with a black-box neural network makes decisions in an opaque manner that hi…
A Dirichlet Process Mixture of Robust Task Models for Scalable Lifelong Reinforcement Learning
Zhi Wang, Chunlin Chen, Daoyi Dong
While reinforcement learning (RL) algorithms are achieving state-of-the-art performance in various challenging tasks, they can easily encounter catastrophic forgetting or interfere…
Efficient Bayesian Policy Reuse with a Scalable Observation Model in Deep Reinforcement Learning
Jinmei Liu, Zhi Wang, Chunlin Chen +1
Bayesian policy reuse (BPR) is a general policy transfer framework for selecting a source policy from an offline library by inferring the task belief based on some observation sign…
Depthwise Convolution for Multi-Agent Communication with Enhanced Mean-Field Approximation
Donghan Xie, Zhi Wang, Chunlin Chen +1
Multi-agent settings remain a fundamental challenge in the reinforcement learning (RL) domain due to the partial observability and the lack of accurate real-time interactions acros…
Rule-Based Reinforcement Learning for Efficient Robot Navigation with Space Reduction
Yuanyang Zhu, Zhi Wang, Chunlin Chen +1
For real-world deployments, it is critical to allow robots to navigate in complex environments autonomously. Traditional methods usually maintain an internal map of the environment…
Measuring Discrimination to Boost Comparative Testing for Multiple Deep Learning Models
Linghan Meng, Yanhui Li, Lin Chen +4
The boom of DL technology leads to massive DL models built and shared, which facilitates the acquisition and reuse of DL models. For a given task, we encounter multiple DL models a…
Multitask Bandit Learning Through Heterogeneous Feedback Aggregation
Zhi Wang, Chicheng Zhang, Manish Kumar Singh +2
In many real-world applications, multiple agents seek to learn how to perform highly related yet slightly different tasks in an online bandit learning protocol. We formulate this p…
Instance Weighted Incremental Evolution Strategies for Reinforcement Learning in Dynamic Environments
Zhi Wang, Chunlin Chen, Daoyi Dong
Evolution strategies (ES), as a family of black-box optimization algorithms, recently emerge as a scalable alternative to reinforcement learning (RL) approaches such as Q-learning…
Lifelong Incremental Reinforcement Learning with Online Bayesian Inference
Zhi Wang, Chunlin Chen, Daoyi Dong
A central capability of a long-lived reinforcement learning (RL) agent is to incrementally adapt its behavior as its environment changes, and to incrementally build upon previous e…