7 citations · 11 across the 5 of their papers we have counts for
5 papers
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…
Follow your Nose: Using General Value Functions for Directed Exploration in Reinforcement Learning
Durgesh Kalwar, Omkar Shelke, Somjit Nath +2
Improving sample efficiency is a key challenge in reinforcement learning, especially in environments with large state spaces and sparse rewards. In literature, this is resolved eit…
School of hard knocks: Curriculum analysis for Pommerman with a fixed computational budget
Omkar Shelke, Hardik Meisheri, Harshad Khadilkar
Pommerman is a hybrid cooperative/adversarial multi-agent environment, with challenging characteristics in terms of partial observability, limited or no communication, sparse and d…
Accelerating Training in Pommerman with Imitation and Reinforcement Learning
Hardik Meisheri, Omkar Shelke, Richa Verma +1
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 2$\times…