169 citations · 728 across the 45 of their papers we have counts for
17 papers · 1 filter
Scaling Laws for Imitation Learning in Single-Agent Games
Jens Tuyls, Dhruv Madeka, Kari Torkkola +3
Imitation Learning (IL) is one of the most widely used methods in machine learning. Yet, many works find it is often unable to fully recover the underlying expert behavior, even in…
On the Complexity of Multi-Agent Decision Making: From Learning in Games to Partial Monitoring
Dylan J. Foster, Dean P. Foster, Noah Golowich +1
A central problem in the theory of multi-agent reinforcement learning (MARL) is to understand what structural conditions and algorithmic principles lead to sample-efficient learnin…
Deep Inventory Management
Dhruv Madeka, Kari Torkkola, Carson Eisenach +3
This work provides a Deep Reinforcement Learning approach to solving a periodic review inventory control system with stochastic vendor lead times, lost sales, correlated demand, an…
Linear Reinforcement Learning with Ball Structure Action Space
Zeyu Jia, Randy Jia, Dhruv Madeka +1
We study the problem of Reinforcement Learning (RL) with linear function approximation, i.e. assuming the optimal action-value function is linear in a known -dimensional feature…
A Few Expert Queries Suffices for Sample-Efficient RL with Resets and Linear Value Approximation
Philip Amortila, Nan Jiang, Dhruv Madeka +1
The current paper studies sample-efficient Reinforcement Learning (RL) in settings where only the optimal value function is assumed to be linearly-realizable. It has recently been…
On Submodular Contextual Bandits
Dean P. Foster, Alexander Rakhlin
We consider the problem of contextual bandits where actions are subsets of a ground set and mean rewards are modeled by an unknown monotone submodular function that belongs to a cl…