Artificial Intelligence: Reinforcement Learning in Python is a complete guide to reinforcement learning with stock trading and online advertising applications.
Reinforcement learning is a ML training method based on rewarding desired behaviours and punishing undesired ones. A reinforcement learning agent can perceive and interpret its environment, take actions and learn through trial and error. Reinforcement learning is largely used in autonomous driving, automated cooling for data centres, recommendation engines, personalised chatbots, stock trading etc.
Here, we look at the top resources to learn reinforcement learning in 2022:
RL course by David Silver
DeepMind research lead David Silver’s course on reinforcement learning taught at University College London is laid out in ten YouTube videos. The videos cover Introduction to Reinforcement Learning, Markov Decision Processes, Planning by Dynamic Programming, Model-Free Prediction, Model-Free Control, Value Function Approximation, Policy Gradient Methods, Integrating Learning and Planning, Exploration and Exploitation, Case Study: RL in Classic Games. To access slides, assignments, exams, check out the link.
Introduction to Reinforcement Learning with Function Approximation
Rich S. Sutton, a research scientist at DeepMind and computing science professor at the University of Alberta, explains the underlying formal problem like the Markov decision processes, core solution methods, dynamic programming, Monte Carlo methods, and temporal-difference learning in this in-depth tutorial.
A History of Reinforcement Learning