What is exploration vs exploitation?

Exploration means trying new actions to discover better options, while exploitation means using known good actions; RL must balance both.

Reinforcement Learning from Human Feedback (RLHF) uses human preferences as rewards to align AI models like chatbots with helpful, safe behavior.

Advanced🖼️ 20 slides⏱ 4 minMachine Learning

🕹️ Reinforcement Learning

Reinforcement learning (RL) trains an agent to make decisions by rewarding good actions and penalizing bad ones. This visual guide covers agents, environments, rewards, policies, exploration vs exploitation, and Q-learning.

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What Is Reinforcement Learning?

An agent learns by trial and error through rewards.

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Learning from Feedback

Good actions are rewarded; bad ones penalized.

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The Agent

The decision-maker that takes actions.

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The Environment

The world the agent interacts with.

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States and Actions

The agent acts based on the current state.

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Rewards

Signals that tell the agent how well it did.

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The Policy

The agent’s strategy for choosing actions.

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Value Functions

Estimate the long-term value of states.

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Exploration vs Exploitation

Try new actions vs use what works.

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The Reward Hypothesis

Goals can be expressed as maximizing reward.

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Trial and Error

The agent improves over many attempts.

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Q-Learning

Learn the value of actions in each state.

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Deep RL

Neural networks handle complex states.

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Delayed Rewards

Actions now affect rewards much later.

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Reward Shaping

Designing rewards is tricky and crucial.

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RL in Games

RL mastered Go, chess, and video games.

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RL in Robotics

Robots learn movement through RL.

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RLHF

RL from human feedback aligns AI models.

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Challenges

Sample efficiency and reward design are hard.

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Getting Started

Try a simple environment like CartPole.

Frequently Asked Questions

Reinforcement learning trains an agent to make decisions by rewarding good actions and penalizing bad ones, so it learns optimal behavior through trial and error.