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Reinforcement learning (RL) is a type of machine learning where an agent learns by interacting with an environment, receiving feedback in the form of rewards or penalties based on its actions. The goal is to learn a policy that maximizes cumulative reward over time.
Key Concepts:
- Agent: The learner or decision maker.
- Environment: The world the agent interacts with.
- State: A representation of the current situation.
- Action: A move the agent can make.
- Reward: Feedback from the environment (positive or negative).
- Policy: A strategy that maps states to actions.
- Goal: Maximize total cumulative reward (also called the "return").
Example Scenario:
Think of training a dog:
- When it sits on command, it gets a treat (reward).
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If it jumps on the couch, it gets scolded (penalty).
Over time, it learns which actions lead to positive outcomes.
Common Applications:
- Game playing (e.g., AlphaGo, chess, Atari)
- Robotics (learning to walk, grasp objects)
- Self-driving cars (learning to navigate safely)
- Recommendation systems (personalized strategies)
Would you like a diagram to show how the agent-environment interaction works?