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The Best Resources to Learn Reinforcement Learning
Explore some of the best (mostly free) tutorials, courses, books, and more on this ever-evolving field
Introduction
Reinforcement learning (RL) is a paradigm of AI methodologies in which an agent learns to interact with its environment in order to maximize the expectation of reward signals received from its environment. Unlike supervised learning, in which the agent is given labeled examples and learns to predict an output based on input, RL involves the agent actively taking actions in its environment and receiving feedback in the form of rewards or punishments. This feedback is used to adjust the agent’s behavior and improve its performance over time.
RL has been applied to a wide range of domains, including robotics, natural language processing, and finance. In the gaming industry, RL has been used to develop advanced game-playing agents, such as the AlphaGo [1] algorithm that defeated a human champion in the board game Go. In the healthcare industry, RL has been used to optimize treatment plans for patients with chronic diseases, such as diabetes. RL has also been used in the field of robotics, allowing robots to learn and adapt to new environments and tasks.
