Neural Inventory Control in Networks via Hindsight Differentiable Policy Optimization

arXiv:2306.11246v2 Announce Type: replace
Abstract: We argue that inventory management presents unique opportunities for reliably applying and evaluating deep reinforcement learning (DRL). Toward reliable application, we emphasize and test two techniques. The first is Hindsight Differentiable Policy Optimization (HDPO), which performs stochastic gradient descent to optimize policy performance while avoiding the need to repeatedly deploy randomized policies in the environment-as is common with generic policy gradient methods. Our second technique involves aligning policy (neural) network architectures with the structure of …

abstract application arxiv control cs.ai cs.lg differentiable gradient inventory management networks opportunities optimization performance policy reinforcement reinforcement learning stochastic test type unique via