Papers by the Columbia research team can be found at Google Scholar.
| Title | Conference | Link | Citations | Year |
|---|---|---|---|---|
| FinRL-Meta: A Universe of Near-Real Market Environments for Data-Driven Deep Reinforcement Learning in Quantitative Finance | NeurIPS 2021 Data-Centric AI Workshop | paper: https://arxiv.org/abs/2112.06753 ; code: https://github.com/AI4Finance-Foundation/FinRL-Meta |
2 | 2021 |
| Explainable deep reinforcement learning for portfolio management: An empirical approach | ICAIF 2021 : ACM International Conference on AI in Finance | paper: https://arxiv.org/abs/2111.03995; code: https://github.com/AI4Finance-Foundation/FinRL |
1 | 2021 |
| FinRL-Podracer: High performance and scalable deep reinforcement learning for quantitative finance | ICAIF 2021 : ACM International Conference on AI in Finance | paper: https://arxiv.org/abs/2111.05188; code: https://github.com/AI4Finance-Foundation/FinRL_Podracer |
2 | 2021 |
| FinRL: Deep reinforcement learning framework to automate trading in quantitative finance | ICAIF 2021 : ACM International Conference on AI in Finance | paper: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3955949; code: https://github.com/AI4Finance-Foundation/FinRL |
7 | 2021 |
| FinRL: A deep reinforcement learning library for automated stock trading in quantitative finance | NeurIPS 2020 Deep RL Workshop | paper: https://arxiv.org/abs/2011.09607; code: https://github.com/AI4Finance-Foundation/FinRL |
25 | 2020 |
| Deep reinforcement learning for automated stock trading: An ensemble strategy | ICAIF 2020 : ACM International Conference on AI in Finance | paper: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3690996; repo: https://github.com/AI4Finance-Foundation/Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020; code: https://github.com/AI4Finance-Foundation/FinRL-Meta/blob/master/tutorials/2-Advance/FinRL_Ensemble_StockTrading_ICAIF_2020/FinRL_Ensemble_StockTrading_ICAIF_2020.ipynb |
46 | 2020 |
| Multi-agent reinforcement learning for liquidation strategy analysis | ICML 2019 Workshop on AI in Finance: Applications and Infrastructure for Multi-Agent Learning | paper: https://arxiv.org/abs/1906.11046; repo: https://github.com/AI4Finance-Foundation/Liquidation-Analysis-using-Multi-Agent-Reinforcement-Learning-ICML-2019; code: https://github.com/AI4Finance-Foundation/FinRL-Meta/blob/master/tutorials/2-Advance/execution_optimizing/execution_optimizing.ipynb |
19 | 2019 |
| Practical deep reinforcement learning approach for stock trading | NeurIPS 2018 Workshop on Challenges and Opportunities for AI in Financial Services | paper: https://arxiv.org/abs/1811.07522; code: https://github.com/AI4Finance-Foundation/DQN-DDPG_Stock_Trading |
87 | 2018 |