"Reinforcement Learning: Shaping AI Through RewardsImagine training an AI agent like teaching a dog tricks. You wouldn't just tell the dog what to do, you'd offer rewards (treats!) for desired behaviors. Reinforcement learning (RL) is a powerful AI technique that uses this same principle. An agent learns through trial and error, taking actions in an environment and receiving rewards or penalties based on the outcome. This allows the agent to develop a strategy, or policy, to maximize its rewards in the long run.The Key Players: Agent, Environment, Actions & Rewards

• Agent: The AI system at the heart of RL, constantly learning and adapting.
• Environment: The agent's playground, whether a physical space like a robot's workspace or a simulated world like a game.
• Actions: The options available to the agent within the environment.
• Rewards: Feedback from the environment, positive for beneficial actions and negative for harmful ones.