17 citations · 31 across the 8 of their papers we have counts for
10 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 Contrastive Samples for Identifying and Leveraging Possible Causal Relationships in Reinforcement Learning
Harshad Khadilkar, Hardik Meisheri
A significant challenge in reinforcement learning is quantifying the complex relationship between actions and long-term rewards. The effects may manifest themselves over a long seq…
A Learning Based Framework for Handling Uncertain Lead Times in Multi-Product Inventory Management
Hardik Meisheri, Somjit Nath, Mayank Baranwal +1
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 scen…
Sample Efficient Training in Multi-Agent Adversarial Games with Limited Teammate Communication
Hardik Meisheri, Harshad Khadilkar
We describe our solution approach for Pommerman TeamRadio, a competition environment associated with NeurIPS 2019. The defining feature of our algorithm is achieving sample efficie…
Reinforcement Learning for Multi-Product Multi-Node Inventory Management in Supply Chains
Nazneen N Sultana, Hardik Meisheri, Vinita Baniwal +3
This paper describes the application of reinforcement learning (RL) to multi-product inventory management in supply chains. The problem description and solution are both adapted fr…
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…