Deep Q-learning for Nash equilibria : Nash-DQN
Year of publication: |
2022
|
---|---|
Authors: | Casgrain, Philippe ; Ning, Brian ; Jaimungal, Sebastian |
Published in: |
Applied mathematical finance. - London : Routledge, ISSN 1466-4313, ZDB-ID 2004159-7. - Vol. 29.2022, 1, p. 62-78
|
Subject: | Reinforcement learning | algorithmic trading | ficticious replay | multi-agent reinforcement learning | Nash equilibria | Nash-Gleichgewicht | Nash equilibrium | Lernprozess | Learning process | Theorie | Theory | Agentenbasierte Modellierung | Agent-based modeling | Lernen | Learning |
-
Dynamic programming principles for mean-field controls with learning
Gu, Haotian, (2023)
-
Multi-agent hierarchical reinforcement learning for energy management
Jendoubi, Imen, (2021)
-
Jendoubi, Imen, (2021)
- More ...
-
Double Deep Q-Learning for optimal execution
Ning, Brian, (2021)
-
Trading algorithms with learning in latent alpha models
Casgrain, Philippe, (2018)
-
Meanāfield games with differing beliefs for algorithmic trading
Casgrain, Philippe, (2020)
- More ...