Reinforcement Learning (RL)

Reinforcement Learning trains agents to make decisions by rewarding desired behaviors and penalizing undesired ones.

• **Key Components:**
• Agent: The learner/decision maker
• Environment: What the agent interacts with
• State: Current situation
• Action: What the agent can do
• Reward: Feedback signal

How It Differs: - Supervised Learning: Learn from labeled examples - Unsupervised Learning: Find patterns in data - Reinforcement Learning: Learn from experience

Famous Examples: - AlphaGo: Beat world Go champion - OpenAI Five: Beat Dota 2 pros - Robot locomotion

In LLMs: - RLHF uses RL to align models with human preferences