Harshad Khadilkar | DeepAI

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Featured Co-authors

Balaraman Ravindran
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Swagat Kumar
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Tanuja Ganu
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Hardik Meisheri
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Mayank Baranwal
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Richa Verma
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Somjit Nath
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Vinita Baniwal
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∙ 05/10/2023

Supplementing Gradient-Based Reinforcement Learning with Simple Evolutionary Ideas

We present a simple, sample-efficient algorithm for introducing large bu...

0 Harshad Khadilkar, et al. ∙

∙ 10/28/2022

Using Contrastive Samples for Identifying and Leveraging Possible Causal Relationships in Reinforcement Learning

A significant challenge in reinforcement learning is quantifying the com...

0 Harshad Khadilkar, et al. ∙

∙ 07/28/2022

A Novel Data Augmentation Technique for Out-of-Distribution Sample Detection using Compounded Corruptions

Modern deep neural network models are known to erroneously classify out-...

0 Ramya S. Hebbalaguppe, et al. ∙

∙ 06/14/2022

Solving the capacitated vehicle routing problem with timing windows using rollouts and MAX-SAT

The vehicle routing problem is a well known class of NP-hard combinatori...

0 Harshad Khadilkar, et al. ∙

∙ 03/02/2022

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

Most existing literature on supply chain and inventory management consid...

4 Hardik Meisheri, et al. ∙

∙ 03/02/2022

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

Exploration versus exploitation dilemma is a significant problem in rein...

8 Somjit Nath, et al. ∙

∙ 12/16/2021

Learning to Minimize Cost-to-Serve for Multi-Node Multi-Product Order Fulfilment in Electronic Commerce

We describe a novel decision-making problem developed in response to the...

0 Pranavi Pathakota, et al. ∙

∙ 08/17/2021

Revisiting State Augmentation methods for Reinforcement Learning with Stochastic Delays

Several real-world scenarios, such as remote control and sensing, are co...

9 Somjit Nath, et al. ∙

∙ 02/24/2021

Fast Approximate Solutions using Reinforcement Learning for Dynamic Capacitated Vehicle Routing with Time Windows

This paper develops an inherently parallelised, fast, approximate learni...

0 Nazneen N Sultana, et al. ∙

∙ 02/23/2021

School of hard knocks: Curriculum analysis for Pommerman with a fixed computational budget

Pommerman is a hybrid cooperative/adversarial multi-agent environment, w...

0 Omkar Shelke, et al. ∙

∙ 11/01/2020

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

We describe our solution approach for Pommerman TeamRadio, a competition...

0 Hardik Meisheri, et al. ∙

∙ 07/01/2020

A Generalized Reinforcement Learning Algorithm for Online 3D Bin-Packing

We propose a Deep Reinforcement Learning (Deep RL) algorithm for solving...

0 Richa Verma, et al. ∙

∙ 06/07/2020

Reinforcement Learning for Multi-Product Multi-Node Inventory Management in Supply Chains

This paper describes the application of reinforcement learning (RL) to m...

30 Nazneen N Sultana, et al. ∙

∙ 04/21/2020

SIBRE: Self Improvement Based REwards for Reinforcement Learning

We propose a generic reward shaping approach for improving rate of conve...

0 Somjit Nath, et al. ∙

∙ 03/31/2020

Optimising Lockdown Policies for Epidemic Control using Reinforcement Learning

In the context of the ongoing Covid-19 pandemic, several reports and stu...

0 Harshad Khadilkar, et al. ∙

∙ 11/12/2019

Accelerating Training in Pommerman with Imitation and Reinforcement Learning

The Pommerman simulation was recently developed to mimic the classic Jap...

0 Hardik Meisheri, et al. ∙

∙ 10/01/2019

Reinforcement Learning for Multi-Objective Optimization of Online Decisions in High-Dimensional Systems

This paper describes a purely data-driven solution to a class of sequent...

26 Hardik Meisheri, et al. ∙