Reinforcement Learning for Time-Series
Reinforcement learning is a widely successful paradigm for control problems and function optimization that doesn't require labeled data. It's a powerful framework for experience-driven autonomous learning, where an agent interacts directly with the environment by taking actions and improves its efficiency by trial and error. Reinforcement learning has been especially popular since the breakthrough of the London-based Google-owned company DeepMind in complex games.
In this chapter, we'll discuss a classification of reinforcement learning (RL) approaches in time-series specifically economics, and we'll deal with the accuracy and applicability of RL-based time-series models.
We'll start with core concepts and algorithms in RL relevant to time-series and we'll talk about open issues and challenges in current deep RL models.
I am going to cover the following topics:
- Introduction to Reinforcement...
