Learn how to create an AI model that plays Super Mario using Python and reinforcement learning in this comprehensive tutorial video. Set up a Mario environment, preprocess the game for applied reinforcement learning, and build a model using the PPO algorithm. Follow step-by-step instructions to implement grayscaling, vectorization, and frame stacking techniques. Explore key concepts such as understanding the Mario state and reward system, installing necessary libraries, and training the reinforcement learning model. By the end of the tutorial, you'll be able to load and use your AI model to play Super Mario autonomously. Access the provided code and additional resources to enhance your understanding of gaming AI and reinforcement learning techniques.
Build a Mario AI Model with Python - Gaming Reinforcement Learning
Overview
Syllabus
- Start
- Introduction
- Explainer
- Client Interview 1
- Animation 1
- Tutorial Start
- Setting Up Mario
- Running the Game
- Understanding the Mario State and Reward
- Client Interview 2
- Preprocessing the Environment
- Installing the RL Libraries
- Applying Grayscaling
- Applying Vectorization
- Applying Frame Stacking
- Client Conversation 3
- Animation 3
- Importing the PPO Algorithm
- Setting Up the Training Callback
- Creating a Mario PPO Model
- Training the Reinforcement Learning Model
- Client Conversation 4
- Animation 4
- Loading the PPO Model
- Using the AI Model
- Client Conversation 5
- Ending
Taught by
Nicholas Renotte
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