This title slide is titled "Reinforcement Learning Fundamentals." Its subtitle welcomes beginners to RL, likening it to training a pet with treats.

Reinforcement Learning Fundamentals

Welcome beginners to RL! Like training a pet with treats. 🌟

This agenda slide, titled "What We'll Cover," outlines the presentation's structure on Reinforcement Learning. It lists topics including RL basics and importance, core components (agent, environment, states, actions, rewards, policy, episodes), the RL interaction loop, real-world examples, and key takeaways.

What We'll Cover

1. What is Reinforcement Learning?

Basic definition and importance of RL.

2. Core Components: Agent & Environment

States, actions, rewards, policy, and episodes explained.

3. The RL Loop

Step-by-step process of agent-environment interaction.

4. Examples & Applications

Real-world uses and practical examples.

5. Key Takeaways

Summary of fundamentals and next steps. Source: Reinforcement Learning Fundamentals

Reinforcement Learning (RL) enables AI to learn by interacting with the world, aiming to maximize rewards over time through trial-and-error. Analogies include a child learning to walk—negative rewards for falls, positive for success—or training a pet with treats.

What is Reinforcement Learning?

• RL: AI learns by interacting with the world
• Goal: Maximize rewards over time
• Analogy: Child learning to walk—falls (negative reward), succeeds (positive)! 👶
• Key: Trial-and-error like training a pet with treats

This slide serves as the header for Section 03: Core Components. It introduces the fundamental building blocks of Reinforcement Learning: Agent, Environment, States, Actions, Rewards, Policy, and Episodes.

03

Core Components

Meet the building blocks of RL: Agent, Environment, States, Actions, Rewards, Policy, Episodes.

The slide introduces the Agent as the learner or decision maker (e.g., robot or game player) that observes surroundings and takes actions to maximize rewards, like a dog deciding to fetch a ball. It contrasts this with the Environment, the world the agent interacts with (e.g., maze or game), which provides states, responds to actions, and delivers rewards, akin to a park where the dog plays and learns.

Agent & Environment

Source: Reinforcement Learning Fundamentals

The slide "Visual: Agent in Environment" shows an agent interacting with its surroundings via an illustrative image. It highlights three key steps: the agent observes the environment state, takes an action, and receives reward feedback from the environment.

Visual: Agent in Environment

!Image

• Agent observes the environment state
• Agent takes an action
• Environment provides feedback as reward

Source: Image from Wikipedia article "Reinforcement learning"

The slide "States: Where Am I?" defines State (S) as the current situation or snapshot of a system. It gives examples like robot position or game score, analogous to your location on a map.

States: Where Am I?

• State (S): Current situation or snapshot
• Examples: Robot position, game score
• Analogy: Your location on a map 📍

The slide "Actions: What Can I Do?" defines actions (A) as the choices available in a given state. It gives examples like moving left/right or jumping, with an analogy to steering wheel turns in a car.

Actions: What Can I Do?

• Actions (A): Choices available in a state
• Examples: Move left/right, jump
• Analogy: Steering wheel turns in a car 🛣️

Source: Reinforcement Learning Fundamentals

The slide "Rewards: The Scorecard" presents a points-based reward system. It gives +1 for reaching goals, -1 penalty for hitting walls, candy for good behavior, and states that high rewards drive better actions.

Rewards: The Scorecard

!Image

• +1 reward for reaching the goal
• -1 penalty for hitting a wall
• Candy 🍭 for good behavior!
• High reward = better action

Source: scoreboard

This slide titled "Policy: The Strategy Guide" outlines a three-step AI workflow using a football analogy. It covers observing the environment state (scout report), consulting the policy for the best action (coach's playbook), and executing the chosen action (quarterback's pass).

Policy: The Strategy Guide

{ "headers": [ "Step", "Process", "Football Analogy" ], "rows": [ [ "Observe State", "Agent sees current environment state (e.g., position, score).", "Scout reports: '3rd down, 10 yards to go, at midfield.'" ], [ "Consult Policy (π)", "Rulebook picks best action for that state.", "Coach checks playbook: 'Run a slant pass.'" ], [ "Choose Action", "Agent takes the selected action.", "Quarterback calls & throws the slant pass." ] ] }

Source: Reinforcement Learning Fundamentals

The timeline depicts an episode starting at t=0 with the agent in initial state S₀, like a game beginning. It then shows the agent taking actions to receive rewards and new states (e.g., at t=1 and repeating), until reaching a terminal end state that completes the full story.

Episodes: A Full Story

t=0: Episode Begins Agent starts in initial state S₀, like game start. t=1: Take Action, Get Feedback Agent acts A₁, gets reward R₁ and new state S₁. t=2: Repeat the Cycle Continues: observe state, act, receive reward and next state. Terminal: Episode Completes Reaches end state, one full story or game over.

The slide depicts the Reinforcement Learning (RL) loop as a workflow table. It outlines the agent observing state St, selecting action At via policy π(St), and the environment providing reward R{t+1} with next state S{t+1}, then repeating.

The RL Loop

{ "headers": [ "Phase", "Entity", "Action/Response" ], "rows": [ [ "Observe State", "Agent", "Perceives current state St from the environment" ], [ "Choose Action", "Agent", "Selects action At using policy π(St)" ], [ "Receive Reward & New State", "Environment", "Provides reward R{t+1} and next state S{t+1} based on (St, At) → Repeat!" ] ] }

The "Real-World Examples" slide showcases four trial-and-error learning demonstrations in a feature grid format. These include navigating a Gridworld Maze, balancing a CartPole, mastering Atari Games, and landing a Rocket.

Real-World Examples

{ "features": [ { "icon": "🗺️", "heading": "Gridworld Maze", "description": "Navigates grid-based maze to goal by trial and error." }, { "icon": "⚖️", "heading": "CartPole Balance", "description": "Balances inverted pole on cart through trial and error." }, { "icon": "🎮", "heading": "Atari Games", "description": "Masters classic Atari games via trial and error play." }, { "icon": "🚀", "heading": "Rocket Landing", "description": "Lands spacecraft softly by trial and error control." } ] }

Source: OpenAI Gym Environments

This slide outlines key Reinforcement Learning (RL) applications, such as AlphaGo mastering Go, robots learning balance, Netflix recommendations, and self-driving cars navigating safely. It highlights the future where AI adapts everywhere.

RL Applications

• Games: AlphaGo masters Go
• Robotics: Walking bots learn balance
• Recommendations: Netflix suggests content
• Self-driving cars: Navigate safely 🚗
• Future: AI adapts everywhere!

The "Why RL Rocks - Quick Stats" slide showcases Reinforcement Learning's strengths with three key metrics. It highlights efficient learning in 1,000s of trials, AlphaGo's wins over Go world champions, and 30% yearly growth in RL applications.

Why RL Rocks - Quick Stats

• 1,000s: Trials to Learn

Efficient trial-error mastery

• World Champs: Beaten in Go

AlphaGo's historic wins

• 30%: Yearly Growth

Rising RL applications

The key takeaway defines Reinforcement Learning (RL) as an agent learning via states, actions, rewards, policy, and episodes. It encourages practicing with simple environments and welcomes questions.

Key Takeaways

RL = Agent learns via States, Actions, Rewards, Policy in Episodes.

Practice with simple envs! Questions? 😊

Source: Reinforcement Learning Fundamentals