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Reinforcement Learning
Reinforcement Learning (RL) is a machine learning paradigm where an agent learns to make decisions by interacting with an environment, receiving rewards or penalties based on its actions.
Core Concepts
- Agent: The learner or decision-maker.
- Environment: The world the agent interacts with.
- State: The current situation the agent observes.
- Action: A choice the agent makes.
- Reward: Feedback signal indicating how good or bad an action was.
- Policy: The strategy the agent uses to choose actions.
The RL Loop
| Step | Description |
|---|---|
| 1 | Agent observes the current state of the environment |
| 2 | Agent selects an action based on its policy |
| 3 | Environment returns a reward and the new state |
| 4 | Agent updates its policy to improve future decisions |
| 5 | Process repeats until the policy converges |
Common RL Algorithms
- Q-Learning: Learns the value of taking a given action in a given state.
- Deep Q-Networks (DQN): Combines Q-learning with deep neural networks.
- Policy Gradient Methods: Directly optimize the policy rather than a value function.
- Proximal Policy Optimization (PPO): A stable, widely used policy gradient algorithm.
Real-World Applications
- Game-playing agents (Chess, Go, video games).
- Robotics and autonomous control systems.
- Dynamic pricing and resource allocation.
- Recommendation systems that adapt to user feedback over time.
Key Takeaway: Unlike supervised learning, reinforcement learning has no fixed "correct answer" — the agent must discover good behavior purely through interaction and reward.
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