254 citations · 320 across the 17 of their papers we have counts for
7 papers · 1 filter
Learning an Inventory Control Policy with General Inventory Arrival Dynamics
Sohrab Andaz, Carson Eisenach, Dhruv Madeka +4
In this paper we address the problem of learning and backtesting inventory control policies in the presence of general arrival dynamics -- which we term as a quantity-over-time arr…
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…
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…
MQRetNN: Multi-Horizon Time Series Forecasting with Retrieval Augmentation
Sitan Yang, Carson Eisenach, Dhruv Madeka
Multi-horizon probabilistic time series forecasting has wide applicability to real-world tasks such as demand forecasting. Recent work in neural time-series forecasting mainly focu…
MQTransformer: Multi-Horizon Forecasts with Context Dependent and Feedback-Aware Attention
Carson Eisenach, Yagna Patel, Dhruv Madeka
Recent advances in neural forecasting have produced major improvements in accuracy for probabilistic demand prediction. In this work, we propose novel improvements to the current s…