Thanks for the discussion thread. In Reinforcement Learning, an agent learns through interactions with the environment in which it finds itself through trial and error. Unlike in supervised learning, where, for instance, the model will be trained on a fixed dataset with already known outputs, RL operates on a system of rewards and punishments. It perceives the consequences of all the actions that an active agent performs in an environment and gets a response in the form of positive or negative rewards. With time, it gains knowledge and learns to perform activities that will maximize the accumulation of rewards. The important terms in RL are the agent (learner), the environment (where it operates), actions (decisions the agent makes), states (the situation of the environment), and rewards (feedback). The agent aims to learn a policy strategy that defines the best action to take in each state. Popular algorithms for reinforcement learning include Q-learning and deep reinforcement learning. It applies neural networks to complex environments with large states and actions, such as video games or autonomous driving.

RL is a cost-intensive technique, but it keeps on coming back for its diverse performance range. The o1 preview model released by Openai uses RL for internal reasoning for the model. I think more exciting innovations are about to come in this field of research.