Reinforcement learning is a subfield of machine learning that involves training algorithms to make decisions in an environment in order to maximize a reward
Reinforcement learning algorithms learn by interacting with their environment and receiving feedback in the form of rewards or punishments.
Reinforcement learning algorithms use a process called trialanderror to learn which actions lead to the greatest rewards
Reinforcement learning algorithms are often used for tasks such as controlling robots or playing games.
Reinforcement learning algorithms can be trained using a variety of techniques like: temporal difference learning, Q-learning, and policy gradient methods
Reinforcement learning algorithms are particularly useful in situations where it is difficult to specify a precise set of rules for the system to follow
One of the key challenges in reinforcement learning is balancing exploration with exploitation