Original Source Here
Q-learning Methods for Financial Engineering: All You Need to Know
How to develop and deploy Q-learning methods with applications, use cases, best practices across reinforcement learning in machine learning
I have never seen another industry that is as sensitive to errors (in the applications of artificial intelligence, AI, as the example) as the industry financial engineers operate.
All that matters in financial engineering is an undiminishing mastery of mathematics.
I have written in the past about how if I could go back and start all over again, agnostic to finance, data science, or product (coding is coding is coding), I would have begun my programming foundations by learning the Hill Climbing algorithm (I wrote about this recently; I will place a link to it at the bottom of this post.)
Q-learning is certainly more advanced. Back to Q-learning.
In financial engineering, there is an ever-growing demand for developing new techniques and tools for solving mathematical problems. One such method that has emerged as an influencer in finance is Q-learning:
It is a reinforcement learning algorithm that applies a model-free teaching procedure  to improve the performance of a given decision rule.
Simply, Q-learning seeks to derive effective decision rules from data.
How Q-learning is different than other AI methods
Traditional supervised learning algorithms require a dataset where the inputs and outputs are known in advance. This is not the case with Q-learning, which can learn from interactions with its environment without needing labeled data.
Q-learning is an example of reinforcement learning, which involves agents  taking actions in an environment in order to maximize some reward. In contrast to supervised learning, there is no need for  pre-labeled datasets; instead, the agent learns by trial and error  from feedback received after each action.
The key difference between Q-learning and other machine learning algorithms lies in the way that rewards are applied to update knowledge about the environment.
In Q-learning, this updating process is done using a so-called Q-function . The Q function gives the expected future reward for taking a given action in a given state; thus, it encodes an agent’s knowledge about its environment into a value. Importantly, this value represents what is important to an agent, like how to maximize its total reward over time.
The relevance for financial engineering
Q-learning is essential in financial engineering because it can help identify and optimize potential trading strategies. As a machine learning algorithm, it can be deployed to select the optimal policy  for a given reinforcement learning problem, making it suited for problems where the reward function is unknown or difficult to determine.
One of the key challenges in financial engineering is designing trading strategies that meet quantitative goals (avoiding saying profits or revenues here) while managing risk. Q-learning can be integrated to develop trading strategies that strike a balance between these two objectives by finding policies that maximize the outcome performance (like returns) while minimizing drawdowns. Additionally, Q-learning can help portfolio managers adapt their investment portfolios to changing market conditions by allowing them quickly retrain their models on new data sets (that emerge or become known over time).
In general, Q-learning can be used for any problem where an agent  needs to learn the optimal behavior in some environment.
In portfolio management: Q-learning could help manage a portfolio of assets by learning the optimal rebalancing strategy for different market conditions. For example, reinforcement learning algorithms have been compared in performance to traditional buy-and-hold strategies (as to how they can or cannot outperform ) during various market conditions.
For asset pricing: Q-learning could be deployed to study and predict asset prices in different markets. This is often accomplished by modeling the environment as a Markov Decision Process (MDP)  and solving for the equilibrium price using dynamic programming methods .
Risk management covers quantifying and managing exposure. Q-Learning could be applied here, too, by helping to identify and quantify risks associated with different investments or portfolios of assets.
Because Q-learning is an off-policy learning algorithm, it may require more data than available to learn the optimal policy, leading to considerations for access to data, expenses, and associated risks in overall model accuracy. As an illustration, Q-learners can sometimes have difficulty converging on the optimal policy due to the curse of dimensionality . Related to the previous point, since each state is represented in memory by a node in the Q-table , Q-learning could potentially require a large amount of memory when compared to other reinforcement learning algorithms such as SARSA (on-policy) .
Simply, use a pre-trained deep learning model that has been trained on a large dataset of historical market data to generate predictions for future market movements. Separately, use a reinforcement learning algorithm that can learn from (past/previous) experience and make predictions about future market movements. A blended approach is to combine both methods, using the strengths of each approach, in an attempt to create an even more accurate prediction model.
Q-learning provides an essential implementation method for financial engineers looking to design and optimize complex systems. While there are many other machine learning algorithms available, few are as well suited to building systems, like trading capabilities, as Q-learning due to its ability to handle large state spaces  and stochastic rewards . As such, incorporating Q-learning into your workflow could provide significant advantages over competing approaches.
Q-learning is robust against changes in the underlying problem data, something that can make this method optimal for implementation across volatile markets where conditions can change rapidly. Since Q-learning is based on learning from experience, it does not require extensive background knowledge about the particular problem being solved, potentially making it more accessible to a wider range of users than other methods.
1. A Q-learning-based dynamic channel assignment technique for mobile communication systems. (n.d.). IEEE Xplore. Retrieved August 2, 2022, from https://ieeexplore.ieee.org/abstract/document/790549
2. Ribeiro. (n.d.). Reinforcement learning agents. Artificial Intelligence Review, 17(3), 223–250. https://doi.org/10.1023/A:1015008417172
3. Sutton et al. Reinforcement Learning Architectures. https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.26.3046&rep=rep1&type=pdf
4. Ohnishi, S., Uchibe, E., Yamaguchi, Y., Nakanishi, K., Yasui, Y., & Ishii, S. (2019). Constrained deep q-learning gradually approaching ordinary q-learning. Frontiers in Neurorobotics, 0. https://doi.org/10.3389/fnbot.2019.00103
5. Watkins, & Dayan. (n.d.). Q-learning. Machine Learning, 8(3), 279–292. https://doi.org/10.1007/BF00992698
6. Hasselt, H. (n.d.). Double q-learning. Advances in Neural Information Processing Systems, 23.
7. A new Q-learning algorithm based on the metropolis criterion. (n.d.). IEEE Xplore. Retrieved August 2, 2022, from https://ieeexplore.ieee.org/abstract/document/1335509
8. Niranjan et al. On-line Q-Learning using connectionist systems. https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.17.2539&rep=rep1&type=pdf
9. Matthew, M., John;Saffell,. (n.d.). Reinforcement learning for trading systems and portfolios. https://www.aaai.org/Papers/KDD/1998/KDD98-049.pdf
10. Safe q-learning method based on constrained Markov decision processes. (n.d.). IEEE Xplore. Retrieved August 2, 2022, from https://ieeexplore.ieee.org/abstract/document/8895829/
11. Klein, Timo. Autonomous algorithmic collusion: Q-learning under sequential pricing. https://onlinelibrary.wiley.com/doi/full/10.1111/1756-2171.12383
12. Neuneier, R. (n.d.). Enhancing q-learning for optimal asset allocation. Advances in Neural Information Processing Systems, 10. See https://proceedings.neurips.cc/paper/1997/hash/970af30e481057c48f87e101b61e6994-Abstract.html
13. Distributed q-learning for dynamically decoupled systems. (n.d.). IEEE Xplore. Retrieved August 2, 2022, from https://ieeexplore.ieee.org/abstract/document/8814663
14. A scalable parallel q-learning algorithm for resource constrained decentralized computing environments. (n.d.). IEEE Xplore. Retrieved August 2, 2022, from https://ieeexplore.ieee.org/abstract/document/7835792
15. Kosana, V., Santhosh, M., Teeparthi, K., & Kumar, S. (2022). A novel dynamic selection approach using on-policy SARSA algorithm for accurate wind speed prediction. Electric Power Systems Research, 108174. https://doi.org/10.1016/j.epsr.2022.108174
16. Singh et al. Using Eligibility Traces to Find the Best Memoryless Policy in Partially Observable Markov Decision Processes. https://www.researchgate.net/profile/Satinder-Singh-3/publication/2396025_Using_Eligibility_Traces_to_Find_the_Best_Memoryless_Policy_in_Partially_Observable_Markov_Decision_Processes/links/55ad05cc08ae98e661a2afb8/Using-Eligibility-Traces-to-Find-the-Best-Memoryless-Policy-in-Partially-Observable-Markov-Decision-Processes.pdf
17. Dittrich, & Fohlmeister. (2020). A deep q-learning-based optimization of the inventory control in a linear process chain. Production Engineering, 15(1), 35–43. https://doi.org/10.1007/s11740-020-01000-8
Trending AI/ML Article Identified & Digested via Granola by Ramsey Elbasheer; a Machine-Driven RSS Bot