In machines, reinforcement learning (RL) is implemented using an agent-environment framework. The agent interacts with an environment by taking actions based on a policy (a strategy for decision-making). The environment provides feedback in the form of rewards or penalties, guiding the agent to improve its actions. Key components include a reward function to evaluate outcomes, a value function to estimate long-term benefits of actions, and exploration strategies to balance learning new behaviors versus exploiting known rewards.