Reinforcement Learning (RL) is a type of Machine Learning where an agent learns by interacting with an environment and receiving rewards or penalties based on its actions.
Instead of being given labeled data, the agent learns through:
- Trial
- Error
- Feedback (reward or punishment)
This is similar to how humans learn through experience.
How Reinforcement Learning Works
RL consists of three main components:
1. Agent
The learner or decision-maker.
2. Environment
Anything the agent interacts with.
3. Actions & Rewards
- Agent takes an action
- Environment responds
- Agent receives reward/penalty
- Agent updates its strategy
This cycle continues until the agent learns the best possible behavior (called the optimal policy).
Key Concepts in Reinforcement Learning
- State: Current situation of the agent
- Action: What the agent chooses to do
- Reward: Feedback from the environment
- Policy: Strategy the agent follows
- Value Function: Expected reward from each state
Popular RL Algorithms
- Q-Learning
- Deep Q Networks (DQN)
- SARSA
- Policy Gradients
- Proximal Policy Optimization (PPO)
Real-World Applications
Reinforcement Learning is used in:
- Self-driving cars (decision-making)
- Robotics (movement and control)
- Games (Chess, Go, Atari, etc.)
- Stock market trading systems
- Recommendation engines
RL is incredibly powerful for tasks requiring continuous learning and real-time decisions.
Conclusion
Reinforcement Learning enables machines to learn through experience, rewards, and trial-and-error. It is one of the most exciting fields in modern AI.
Citations
https://savanka.com/category/learn/ai-and-ml/
https://www.w3schools.com/ai/