Search Results for author:
Found 4 papers, 1 papers with code
IMM: An Imitative Reinforcement Learning Approach with Predictive Representation Learning for Automatic Market Making
Market making (MM) has attracted significant attention in financial trading owing to its essential function in ensuring market liquidity.
Multimodal Representations Learning Based on Mutual Information Maximization and Minimization and Identity Embedding for Multimodal Sentiment Analysis
Multimodal sentiment analysis (MSA) is a fundamental complex research problem due to the heterogeneity gap between different modalities and the ambiguity of human emotional expression.
ElegantRL-Podracer: Scalable and Elastic Library for Cloud-Native Deep Reinforcement Learning
In this paper, we present a scalable and elastic library ElegantRL-podracer for cloud-native deep reinforcement learning, which efficiently supports millions of GPU cores to carry out massively parallel training at multiple levels.
FinRL-Podracer: High Performance and Scalable Deep Reinforcement Learning for Quantitative Finance
Unfortunately, the steep learning curve and the difficulty in quick modeling and agile development are impeding finance researchers from using deep reinforcement learning in quantitative trading.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
