174 citations · 621 across the 31 of their papers we have counts for
32 papers
Achieving Fairness in Multi-Agent Markov Decision Processes Using Reinforcement Learning
Peizhong Ju, Arnob Ghosh, Ness B. Shroff
Fairness plays a crucial role in various multi-agent systems (e.g., communication networks, financial markets, etc.). Many multi-agent dynamical interactions can be cast as Markov…
Provably Efficient Model-Free Algorithms for Non-stationary CMDPs
Honghao Wei, Arnob Ghosh, Ness Shroff +2
We study model-free reinforcement learning (RL) algorithms in episodic non-stationary constrained Markov Decision Processes (CMDPs), in which an agent aims to maximize the expected…
Pricing in Ride-sharing Markets : Effects of network competition and autonomous vehicles
Diptangshu Sen, Arnob Ghosh
Autonomous vehicles will be an integral part of ride-sharing services in the future. This setting differs from traditional ride-sharing marketplaces because of the absence of the s…
Provably Efficient Model-free RL in Leader-Follower MDP with Linear Function Approximation
Arnob Ghosh
We consider a multi-agent episodic MDP setup where an agent (leader) takes action at each step of the episode followed by another agent (follower). The state evolution and rewards…
Interference Constrained Beam Alignment for Time-Varying Channels via Kernelized Bandits
Yuntian Deng, Xingyu Zhou, Arnob Ghosh +2
To fully utilize the abundant spectrum resources in millimeter wave (mmWave), Beam Alignment (BA) is necessary for large antenna arrays to achieve large array gains. In practical d…
Provably Efficient Model-Free Constrained RL with Linear Function Approximation
Arnob Ghosh, Xingyu Zhou, Ness Shroff
We study the constrained reinforcement learning problem, in which an agent aims to maximize the expected cumulative reward subject to a constraint on the expected total value of a…