169 citations · 728 across the 45 of their papers we have counts for
47 papers
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…
Forecast Hedging and Calibration
Dean P. Foster, Sergiu Hart
Calibration means that forecasts and average realized frequencies are close. We develop the concept of forecast hedging, which consists of choosing the forecasts so as to guarantee…
Smooth Calibration, Leaky Forecasts, Finite Recall, and Nash Dynamics
Dean P. Foster, Sergiu Hart
We propose to smooth out the calibration score, which measures how good a forecaster is, by combining nearby forecasts. While regular calibration can be guaranteed only by randomiz…