most citedDeep Reinforcement Learning for Inventory Networks: Toward Reliable Policy Optimization

3 citations · 3 across the 2 of their papers we have counts for

collaborators

2 papers

cs.LG2026

Policy Optimization in Hybrid Discrete-Continuous Action Spaces via Mixed Gradients

Matias Alvo, Daniel Russo, Yash Kanoria

We study reinforcement learning in hybrid discrete-continuous action spaces, such as settings where the discrete component selects a regime (or index) and the continuous component…

cs.LG2023★ 3 cited

Deep Reinforcement Learning for Inventory Networks: Toward Reliable Policy Optimization

Matias Alvo, Daniel Russo, Yash Kanoria +1

We argue that inventory management presents unique opportunities for the reliable application of deep reinforcement learning (DRL). To enable this, we emphasize and test two comple…