Reinforcement Learning
40 min read
RL Fundamentals
Learning from rewards
Supervised learning needs labeled examples. But what if you just have a goal and a way to measure success? Reinforcement learning teaches AI through trial, error, and reward—the same way humans and animals learn.
Learning from Rewards
In reinforcement learning, an agent takes actions in an environment. Each action leads to a new state and a reward. The agent's goal: maximize cumulative reward over time. This simple framework—try things, see what works, do more of that—produces surprisingly sophisticated behavior.
The Explore-Exploit Tradeoff
Should the agent try new things (explore) or stick with what works (exploit)? Too much exploration wastes time. Too much exploitation misses better strategies. Balancing this tradeoff is central to RL. Techniques like epsilon-greedy, UCB, and entropy bonuses help manage this balance.
Why RL Matters Now
RL powers game-playing AI (AlphaGo, OpenAI Five), robotic control, recommendation systems, and increasingly, language model alignment (RLHF). If you want to shape AI behavior toward goals rather than just mimicking examples, RL is the answer.
💡 Key Takeaways
- RL learns from rewards, not labeled examples
- Explore vs exploit: the central RL tradeoff
- Powers games, robotics, recommendations, and RLHF
- Essential for goal-directed AI behavior
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