Machine Learning
Reinforcement learning (RL)
Reinforcement learning (RL) is a type of machine learning where an agent learns to make decisions in an environment to maximize a cumulative reward. It differs from supervised learning by not requiring labeled input/output pairs, instead learning through trial and error.
Explanation
Reinforcement learning involves an agent interacting with an environment. The agent observes the environment's state, takes an action, and receives a reward (or penalty) in return. The agent's goal is to learn an optimal policy, which is a mapping from states to actions, that maximizes the expected cumulative reward over time. This is achieved through iterative processes where the agent explores different actions and exploits the actions that yield higher rewards. Common RL algorithms include Q-learning, SARSA, and Deep Q-Networks (DQN). RL is used in various applications like robotics, game playing (e.g., AlphaGo), and resource management due to its ability to learn complex behaviors without explicit programming.