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07/11/2023
Using Linear Regression for Iteratively Training Neural Networks
We present a simple linear regression based approach for learning the we...
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06/28/2023
DCT: Dual Channel Training of Action Embeddings for Reinforcement Learning with Large Discrete Action Spaces
The ability to learn robust policies while generalizing over large discr...
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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...
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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...
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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-...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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07/01/2020
A Generalized Reinforcement Learning Algorithm for Online 3D Bin-Packing
We propose a Deep Reinforcement Learning (Deep RL) algorithm for solving...
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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...
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04/21/2020
SIBRE: Self Improvement Based REwards for Reinforcement Learning
We propose a generic reward shaping approach for improving rate of conve...
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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...
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11/12/2019
Accelerating Training in Pommerman with Imitation and Reinforcement Learning
The Pommerman simulation was recently developed to mimic the classic Jap...
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10/01/2019