Reinforcement Learning is a type of machine learning where an agent learns to make decisions by interacting with an environment. The agent receives feedback in the form of rewards or penalties as it navigates through the problem space, aiming to maximize cumulative rewards over time.
- Glossary > Letter: R
What does "Reinforcement Learning" mean?
Use Cases
Game Playing:
Teaching agents to play games like chess or Go through trial and error.
Robotics:
Training robots to perform tasks such as walking or object manipulation autonomously.
Resource Management:
Optimizing decisions in dynamic and uncertain environments.
Importance
Autonomy:
Enables agents to learn and adapt to new situations without human intervention.
Sequential Decision Making:
Models complex decision-making processes over time with delayed rewards.
Exploration and Exploitation:
Balances between exploring new actions and exploiting known strategies to achieve optimal results.
Analogies
Reinforcement Learning is like teaching a dog new tricks through rewards and punishments. Just as a dog learns behaviors that lead to rewards and avoids behaviors that lead to punishment, reinforcement learning agents learn actions that maximize rewards in their environments.
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