6 papers
Ask to Learn: Query-Based Collaborative Reinforcement Learning
Kaicheng Zhang, Hongqian Niu, Laixi Shi +3
Offline reinforcement learning is fundamentally limited by the coverage of the available data, while collecting additional trajectories through online exploration may be costly, un…
Learning Fractional-Order Dynamics from a Single Trajectory
Xiaole Zhang, Ziyi Zhang, Zehao Zhao +4
Many real-world processes exhibit long-range dependence, where the current state depends on a slowly decaying trace of past states rather than on the most recent state alone. This…
Natural Policy Gradient for Average Reward Non-Stationary RL
Neharika Jali, Eshika Pathak, Pranay Sharma +2
We consider the problem of non-stationary reinforcement learning (RL) in the infinite-horizon average-reward setting. We model it by a Markov Decision Process with time-varying rew…
Mean-Field Sampling for Cooperative Multi-Agent Reinforcement Learning
Emile Anand, Ishani Karmarkar, Guannan Qu
Designing efficient algorithms for multi-agent reinforcement learning (MARL) is fundamentally challenging because the size of the joint state and action spaces grows exponentially…
Efficient Reinforcement Learning for Global Decision Making in the Presence of Local Agents at Scale
Emile Anand, Guannan Qu
We study reinforcement learning for global decision-making in the presence of local agents, where the global decision-maker makes decisions affecting all local agents, and the obje…
Efficient Reinforcement Learning for Routing Jobs in Heterogeneous Queueing Systems
Neharika Jali, Guannan Qu, Weina Wang +1
We consider the problem of efficiently routing jobs that arrive into a central queue to a system of heterogeneous servers. Unlike homogeneous systems, a threshold policy, that rout…