Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Mani Shankar, M.; Sweety, A.; Deepthi, Das Title A name given to the resource Optimizing Algorithmic Trading Through DRL: A Comparative Analysis of Single-Agent and Multi-Agent Models Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1723 LNNS;pp.1-15 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-032-10783-1_1" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-10783-1_1</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105028320509?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105028320509?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Mani Shankar M., Department of Statistics and Data Science, Christ University, Bengaluru, India; Sweety A., Department of Statistics and Data Science, Christ University, Bengaluru, India; Deepthi D., Department of Statistics and Data Science, Christ University, Bengaluru, India Description An account of the resource This work investigates how Deep Reinforcement Learning (DRL) can elevate algorithmic tradingespecially in fast-paced, high-frequency markets. We propose a full-fledged framework to compare different setups, from solo agents to multi-agent systems, applying DRL methods like Proximal Policy Optimization (PPO), Deep Q-Network (DQN), and Advantage Actor-Critic (A2C), along with combinations of these. We trained on hourly stock data from 24 firms over two years (Jan 1, 2020Jan 1, 2022) and tested performance over the next year (Jan 1, 2022Jan 1, 2023). We evaluated key factorsreturns, risk control, and how well these models adapt to changing markets. The single-agent PPO model stood out, achieving a remarkable profit factor of 28.07 on BIDU and keeping peak drawdowns frequently under 1%. This demonstrates both solid capital protection and high risk-adjusted performance. Ensemble models showed balanced performance in both single-agent and multi-agent setups, achieving a Sharpe ratio of 0.75 and Sortino ratios up to 7.7, outperforming existing benchmarks. Comparative analyses revealed that ensemble strategies enhance market responsiveness and improve both stability and profitability in volatile environments. Sensitivity analysis confirmed the robustness of model performance across various hyperparameter settings. Overall, the proposed DRL-based ensemble framework demonstrates strong potential to improve real-world HFT systems by delivering more adaptive, stable, and efficient algorithmic trading solutions. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. Subject The topic of the resource Algorithmic trading; Deep reinforcement learning; Ensemble models; High-frequency trading; Proximal policy optimization Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-303210782-4; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online