👉 Reinforcement learning (RL) is a subset of machine learning where an agent learns to make decisions by interacting with an environment to maximize cumulative rewards. Unlike supervised learning, which relies on labeled data, RL involves trial and error, allowing the agent to learn optimal behaviors through feedback in the form of rewards or penalties. The agent observes the state of the environment, takes actions, and receives outcomes that guide its future decisions. Over time, the agent refines its strategy to achieve long-term goals, making it particularly useful for complex tasks like game playing, robotics, and autonomous navigation where explicit instructions are difficult to define. Key components include the policy (the strategy the agent uses), the value function (estimating the expected future rewards), and the reward signal (providing feedback on actions).
