Hands-On Machine Learning for Algorithmic Trading

Reinforcement learning

Reinforcement learning is the third type of ML. It aims to choose the action that yields the highest reward, given a set of input data that describes a context or environment. It is both dynamic and interactive: the stream of positive and negative rewards impacts the algorithm's learning, and actions taken now may influence both the environment and future rewards.

The trade-off between the exploitation of a course of action that has been learned to yield a certain reward and the exploration of new actions that may increase the reward in the future gives rise to a trial-and-error approach. Reinforcement learning optimizes the agent's learning using dynamical systems theory and, in particular, the optimal control of ...