Reinforcement Learning

A Learning Based Framework for Handling Uncertain Lead Times in Multi-Product Inventory Management

Most existing literature on supply chain and inventory management consider stochastic demand processes with zero or constant lead times. While it is true that in certain niche scenarios, uncertainty in lead times can be ignored, most real-world …

Follow your Nose: Using General Value Functions for Directed Exploration in Reinforcement Learning

Exploration versus exploitation dilemma is a significant problem in reinforcement learning (RL), particularly in complex environments with large state space and sparse rewards. When optimizing for a particular goal, running simple smaller tasks can …

FoLaR: Foggy Latent Representations for Reinforcement Learning with Partial Observability

We propose a novel methodology for improving the rate and consistency of reinforcement learning in partially observable (foggy) environments, under the broader umbrella of robust latent representations. The present work addresses partially observable …

Scalable multi-product inventory control with lead time constraints using reinforcement learning

Determining optimum inventory replenishment decisions is critical for retail businesses with uncertain demand. The problem becomes particularly challenging when multiple products with different lead times and cross-product constraints are considered. …

Sample Efficient Training in Multi-Agent Adversarial Games with Limited Teammate Communication

We describe our solution approach for Pommerman TeamRadio, a competition environment associated with NeurIPS 2019. The defining feature of our algorithm is achieving sample efficiency within a restrictive computational budget while beating the …

Using Reinforcement Learning for a Large Variable-Dimensional Inventory Management Problem

This paper evaluates the applicability of reinforcement learning (RL) to multi-product inventory management in supply chains. The novelty of this problem with respect to supply chain literature is (i) we consider concurrent inventory management of a …

Sample Efficient RL

Making Model-free RL sample efficient

Accelerating Training in Pommerman with Imitation and Reinforcement Learning

The Pommerman simulation was recently developed to mimic the classic Japanese game Bomberman, and focuses on competitive gameplay in a multi-agent setting. We focus on the 22 team version of Pommerman, developed for a competition at NeurIPS 2018. Our …

Actor Based Simulation for Closed Loop Control of Supply Chain using Reinforcement Learning

Reinforcement Learning (RL) has achieved a degree of success in control applications such as online gameplay and robotics, but has rarely been used to manage operations of business-critical systems such as supply chains. A key aspect of using RL in …

Reinforcement Learning in Supply Chain Optimization

Applicability of reinforcement learning (RL) algorithms to a class of problems rarely addressed in machine learning literature, involving the control of a dynamic system with high-dimensional control inputs (actions).