Foundations • Part 2 of 3
✅Editor Reviewed

RL in the Wild

Real-world examples of reinforcement learning all around us

RL is Everywhere

Once you understand the RL framework, you start seeing it everywhere. Let’s explore real examples—from everyday decisions to cutting-edge AI systems.

The RL pattern appears whenever there’s:

  • An entity making sequential decisions
  • Feedback that indicates success or failure
  • A goal to optimize over time

Everyday RL: Decisions You Make Daily

🚗
Your Morning Commute
State: Time of day, weather, calendar
Actions: Route A, B, or C; leave now or wait
Reward: Negative of commute time

You’ve learned that Route B is faster on rainy Mondays

🍽️
Choosing Where to Eat
State: Hunger level, mood, who you’re with
Actions: Go to known spot vs. try somewhere new
Reward: Meal satisfaction

Classic exploration-exploitation: stick with favorites or discover new gems?

🎸
Learning an Instrument
State: Current skill level, muscle memory
Actions: Finger positions, strumming patterns
Reward: Sound quality, hitting the right notes

Delayed rewards: hours of practice before you sound good

💬
Having a Conversation
State: Conversation history, social context, body language
Actions: What to say next, tone, topic changes
Reward: Engagement, laughter, connection

Highly sequential: each response shapes the next state

LLMs and Conversations: RL in Action

📌Large Language Models are RL Agents

When you chat with an AI assistant like ChatGPT or Claude, you’re interacting with a system trained using reinforcement learning. This is one of the most impactful applications of RL today.

The LLM as an RL Agent
State
The conversation so far (your messages + AI responses)
Action
The next token (word/piece) to generate
Reward
Human preference signals: thumbs up/down, chosen responses
Policy
The neural network that decides what to say next

RLHF (Reinforcement Learning from Human Feedback) is how modern LLMs learn to be helpful:

  1. Pre-training: Learn language patterns from massive text datasets
  2. Reward modeling: Humans rank AI responses; a reward model learns these preferences
  3. RL fine-tuning: The LLM is optimized to generate responses the reward model scores highly

This is why AI assistants got dramatically better at following instructions and being helpful—RL made the difference.

ℹ️Why RL for LLMs?

Supervised learning alone can’t optimize for “helpfulness” or “honesty”—these are subjective qualities that emerge from human judgment. RL bridges this gap by learning directly from human preferences, not just text examples.

Industry Applications

🎮
Game AI

Superhuman performance in complex games

  • • AlphaGo/AlphaZero (Go, Chess)
  • • OpenAI Five (Dota 2)
  • • AlphaStar (StarCraft II)
🤖
Robotics

Learning physical skills from scratch

  • • Dexterous manipulation
  • • Locomotion and walking
  • • Autonomous drones
📱
Recommendations

Personalizing content and ads

  • • YouTube/TikTok feeds
  • • E-commerce suggestions
  • • Ad targeting
🏭
Infrastructure & Operations
  • Data center cooling — Google reduced energy use 40%
  • Traffic signal control — Adaptive timing reduces congestion
  • Inventory management — Dynamic pricing and stocking
🔬
Scientific Discovery
  • Drug design — Optimizing molecular structures
  • Chip layout — Google’s TPU design optimization
  • Fusion control — Plasma containment in reactors

The Pattern: When to Think “RL”

💡RL Might Be Right When...

Ask yourself these questions about your problem:

  1. Sequential decisions? — Actions now affect future options
  2. Delayed feedback? — You don’t know immediately if a choice was good
  3. No labeled “correct” answers? — Just outcomes to evaluate
  4. Need to balance exploration? — Unknown options might be better

If you answered yes to most of these, you might have an RL problem.

✓
Good fit for RL
Game playing, robotics, dialogue systems, resource allocation, adaptive control
✗
Probably overkill
Image classification, sentiment analysis, one-shot predictions—use supervised learning instead