Learn the fundamentals of Reinforcement Learning with Python in this 26-minute tutorial. Discover how to create an OpenAI gym environment and train a reinforcement learning algorithm to solve the Lunar Lander problem using stable_baselines algorithms. Follow along as the instructor guides you through installing Stable Baselines, training a model using the ACER Algorithm, and evaluating the model on LunarLander-v2. Gain practical skills in implementing and assessing deep reinforcement learning models, which can be applied to various RL problems. Access the provided code repository and additional resources to further enhance your understanding of this powerful machine learning technique.
Python Reinforcement Learning Tutorial for Beginners
Overview
Syllabus
- Start
- Reinforcement Learning Flow
- Installing Python Dependencies
- Importing RL Dependencies including stable_baselines
- Testing the LunarLander-v2 Environment
- Training an ACER Reinforcement Learning Model
- Evaluating the Model
- Saving and Reloading RL Model Weights
Taught by
Nicholas Renotte
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