What is reinforcement learning (RL)?
Reinforcement learning learns by trial and reward.
How It Works:
RL agents interact with an environment, receive rewards for good actions, and learn policies that maximize cumulative rewards over time.
Key Benefits:
- Autonomous optimization: Learns strategies without labeled data.
- Complex tasks: Handles sequential decision-making.
- Exploration: Discovers novel solutions.
Real-World Use Cases:
- Robotics: Teach robots to grasp objects.
- Game AI: Master complex games like Go or Dota.
FAQs
How differ from supervised learning?
Is RL sample-efficient?
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