10 papers
Multi-Agent Reinforcement Learning via Agent-Specific Preference
Ni Mu, Yao Luan, Yiqin Yang +1
Multi-agent reinforcement learning (MARL) is a powerful framework for solving complex collaborative tasks, but it relies heavily on well-defined global reward functions. Designing…
Sim2O: Efficient Offline-to-Online MARL via Joint Action Composition
Bingchang Song, Yiqin Yang
Offline-to-online adaptation serves as a pivotal paradigm for mitigating the prohibitive cost of online exploration by bootstrapping reinforcement learning from offline datasets. W…
Curriculum reinforcement learning with measurable task representation learning
Yongyan Wen, Siyuan Li, Mingjian Fu +3
In curriculum reinforcement learning (CRL), an agent incrementally accumulates knowledge over a sequence of tasks (i.e., a curriculum), and the learning process is aimed at using t…
Data-Enabled Policy and Value Iteration for Continuous-Time Linear Quadratic Output Feedback Control
Jun Xie, Yuan-Hua Ni, Yiqin Yang +1
This paper proposes efficient policy iteration and value iteration algorithms for the continuous-time linear quadratic regulator problem with unmeasurable states and unknown system…
OPRIDE: Offline Preference-based Reinforcement Learning via In-Dataset Exploration
Yiqin Yang, Hao Hu, Yihuan Mao +10
Preference-based reinforcement learning (PbRL) can help avoid sophisticated reward designs and align better with human intentions, showing great promise in various real-world appli…
DAIL: Beyond Task Ambiguity for Language-Conditioned Reinforcement Learning
Runpeng Xie, Quanwei Wang, Hao Hu +7
Comprehending natural language and following human instructions are critical capabilities for intelligent agents. However, the flexibility of linguistic instructions induces substa…