26 citations · 69 across the 34 of their papers we have counts for
7 papers · 1 filter
Heuristic for Optimisation of Dark Store Facility Locations for Quick Commerce Businesses
Prithvi Kewalramani, Harshad Khadilkar
We present a fast, flexible heuristic for setting up warehouse locations for quick commerce businesses, with the goal of serving the largest number of customers under the constrain…
Reinforcement Replaces Supervision: Query focused Summarization using Deep Reinforcement Learning
Swaroop Nath, Harshad Khadilkar, Pushpak Bhattacharyya
Query-focused Summarization (QfS) deals with systems that generate summaries from document(s) based on a query. Motivated by the insight that Reinforcement Learning (RL) provides a…
Multi-Agent Learning of Efficient Fulfilment and Routing Strategies in E-Commerce
Omkar Shelke, Pranavi Pathakota, Anandsingh Chauhan +3
This paper presents an integrated algorithmic framework for minimising product delivery costs in e-commerce (known as the cost-to-serve or C2S). One of the major challenges in e-co…
Using General Value Functions to Learn Domain-Backed Inventory Management Policies
Durgesh Kalwar, Omkar Shelke, Harshad Khadilkar
We consider the inventory management problem, where the goal is to balance conflicting objectives such as availability and wastage of a large range of products in a store. We propo…
Using Linear Regression for Iteratively Training Neural Networks
Harshad Khadilkar
We present a simple linear regression based approach for learning the weights and biases of a neural network, as an alternative to standard gradient based backpropagation. The pres…
DCT: Dual Channel Training of Action Embeddings for Reinforcement Learning with Large Discrete Action Spaces
Pranavi Pathakota, Hardik Meisheri, Harshad Khadilkar
The ability to learn robust policies while generalizing over large discrete action spaces is an open challenge for intelligent systems, especially in noisy environments that face t…
Supplementing Gradient-Based Reinforcement Learning with Simple Evolutionary Ideas
Harshad Khadilkar
We present a simple, sample-efficient algorithm for introducing large but directed learning steps in reinforcement learning (RL), through the use of evolutionary operators. The met…