Reinforcement Learning

Reinforcement Learning is a machine learning paradigm where an agent learns optimal behavior through trial-and-error interactions with an environment to maximize cumulative rewards. Unlike supervised learning that relies on labeled datasets, reinforcement learning uses a feedback system of rewards and penalties to guide decision-making. The agent observes environmental states, takes actions, and receives rewards or punishments, gradually developing strategies that maximize long-term returns. Key components include the agent, environment, actions, states, and reward function. Common algorithms include Q-learning, policy gradients, and actor-critic methods. This approach has achieved breakthrough results in game playing (AlphaGo, chess), robotics, autonomous vehicles, and resource allocation problems. Reinforcement learning is fundamental to developing AI systems that can adapt and improve performance in dynamic, uncertain environments without explicit programming.