What is Reinforcement Learning?
Reinforcement Learning (RL) is a subset of Machine Learning, which is itself a branch of Artificial Intelligence (AI). RL focuses on how agents should take actions in an environment to maximize cumulative reward. It is fundamentally different from supervised learning, where the model is trained on a fixed dataset with known outputs.
In RL, an agent interacts with its environment, receiving feedback in the form of rewards or penalties based on its actions. The goal of the agent is to learn a policy that dictates the best action to take in each state to maximize the total expected reward over time. This learning paradigm is inspired by behavioral psychology, where organisms learn from the consequences of their actions.
The architecture of reinforcement learning often involves neural networks, particularly in complex environments where state and action spaces are large. Deep Reinforcement Learning combines neural networks with RL principles to enable machines to learn from high-dimensional sensory inputs, such as images and complex data, making it applicable to various fields including robotics, gaming, and autonomous systems.
Key concepts in reinforcement learning include exploration versus exploitation, discount factors, value functions, and Q-learning. Through iterative learning, agents can develop strategies for optimal decision-making, paving the way for advancements in technology and AI applications.