3 citations · 3 across the 4 of their papers we have counts for
5 papers
Trust Region Reward Optimization and Proximal Inverse Reward Optimization Algorithm
Yang Chen, Menglin Zou, Jiaqi Zhang +6
Inverse Reinforcement Learning (IRL) learns a reward function to explain expert demonstrations. Modern IRL methods often use the adversarial (minimax) formulation that alternates b…
Meta-Inverse Reinforcement Learning for Mean Field Games via Probabilistic Context Variables
Yang Chen, Xiao Lin, Bo Yan +4
Designing suitable reward functions for numerous interacting intelligent agents is challenging in real-world applications. Inverse reinforcement learning (IRL) in mean field games…
Learning Density-Based Correlated Equilibria for Markov Games
Libo Zhang, Yang Chen, Toru Takisaka +3
Correlated Equilibrium (CE) is a well-established solution concept that captures coordination among agents and enjoys good algorithmic properties. In real-world multi-agent systems…
MSDC: Exploiting Multi-State Power Consumption in Non-intrusive Load Monitoring based on A Dual-CNN Model
Jialing He, Jiamou Liu, Zijian Zhang +4
Non-intrusive load monitoring (NILM) aims to decompose aggregated electrical usage signal into appliance-specific power consumption and it amounts to a classical example of blind s…
Individual-Level Inverse Reinforcement Learning for Mean Field Games
Yang Chen, Libo Zhang, Jiamou Liu +1
The recent mean field game (MFG) formalism has enabled the application of inverse reinforcement learning (IRL) methods in large-scale multi-agent systems, with the goal of inferrin…