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Found 16 papers, 1 papers with code
Multi-Agent Learning of Efficient Fulfilment and Routing Strategies in E-Commerce
This paper presents an integrated algorithmic framework for minimising product delivery costs in e-commerce (known as the cost-to-serve or C2S).
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 discrete action spaces is an open challenge for intelligent systems, especially in noisy environments that face the curse of dimensionality.
Using Contrastive Samples for Identifying and Leveraging Possible Causal Relationships in Reinforcement Learning
A significant challenge in reinforcement learning is quantifying the complex relationship between actions and long-term rewards.
A Learning Based Framework for Handling Uncertain Lead Times in Multi-Product Inventory Management
Through empirical evaluations, it is further shown that the inventory management with uncertain lead times is not only equivalent to that of delay in information sharing across multiple echelons (\emph{observation delay}), a model trained to handle one kind of delay is capable to handle delays of another kind without requiring to be retrained.
Follow your Nose: Using General Value Functions for Directed Exploration in Reinforcement Learning
Exploration methods have been used to sample better trajectories in large environments while auxiliary tasks have been incorporated where the reward is sparse.
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 demands of retail electronic commerce (e-commerce).
School of hard knocks: Curriculum analysis for Pommerman with a fixed computational budget
In this paper, we focus on developing a curriculum for learning a robust and promising policy in a constrained computational budget of 100, 000 games, starting from a fixed base policy (which is itself trained to imitate a noisy expert policy).
Sample Efficient Training in Multi-Agent Adversarial Games with Limited Teammate Communication
We describe our solution approach for Pommerman TeamRadio, a competition environment associated with NeurIPS 2019.
Reinforcement Learning for Multi-Product Multi-Node Inventory Management in Supply Chains
This paper describes the application of reinforcement learning (RL) to multi-product inventory management in supply chains.
Accelerating Training in Pommerman with Imitation and Reinforcement Learning
Our methodology involves training an agent initially through imitation learning on a noisy expert policy, followed by a proximal-policy optimization (PPO) reinforcement learning algorithm.
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 sequential decision-making problems with a large number of concurrent online decisions, with applications to computing systems and operations research.
Learning representations for sentiment classification using Multi-task framework
Most of the existing state of the art sentiment classification techniques involve the use of pre-trained embeddings.
TCS Research at SemEval-2018 Task 1: Learning Robust Representations using Multi-Attention Architecture
This paper presents system description of our submission to the SemEval-2018 task-1: Affect in tweets for the English language.
Multiclass Common Spatial Pattern for EEG based Brain Computer Interface with Adaptive Learning Classifier
In addition to this non-stationarity present in the features extracted from the CSP present a challenge in classification.
Multi-Document Summarization using Distributed Bag-of-Words Model
As the number of documents on the web is growing exponentially, multi-document summarization is becoming more and more important since it can provide the main ideas in a document set in short time.
Textmining at EmoInt-2017: A Deep Learning Approach to Sentiment Intensity Scoring of English Tweets
This paper describes our approach to the Emotion Intensity shared task.
