4 papers
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…
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…
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…
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…