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PROJECT
Teaching a Robot to Walk Using Deep Reinforcement Learning
In this project, we’ll train a bipedal robot to autonomously learn how to walk using a deep reinforcement learning algorithm, built from scratch. We'll use the Gymnasium library to simulate the environment and PyTorch to train the agent’s policy network.
You will learn to:
Perform deep reinforcement learning.
Train and test a REINFORCE agent.
Work with the Gymnasium API.
Work with the MuJoCo environments.
Skills
Machine Learning
Deep Learning
Reinforcement Learning
Robotics
Prerequisites
Programming in Python
Basic knowledge of deep learning
Building and training a neural network in PyTorch
Technologies
Python
PyTorch
Gymnasium
Matplotlib
Project Description
In this project, we’ll use deep reinforcement learning to train a two-legged robot to walk forward in a virtual environment. We’ll first learn how to work with the Gymnasium API (formerly OpenAI Gym), specifically the Walker2d environment, which provides a realistic simulation of a walking robot.
Next, we’ll implement a policy-gradient algorithm called REINFORCE from scratch using PyTorch and use it to train and test the robot. This approach will involve designing a neural network that learns optimal policies through trial and error, gradually improving the agent’s performance based on the rewards received. Finally, we’ll plot the learning curve of the algorithm and display the learned policy in the environment.
Project Tasks
1
Introduction
Task 0: Introduction
2
Exploring the MuJoCo Environments
Task 1: Explore the InvertedPendulum Environment
Task 2: Explore the Walker2d Environment
3
Building the REINFORCE Agent
Task 3: Build the Policy Network
Task 4: Implement the REINFORCE Algorithm
Task 5: Build the AgentTrainer Class
4
Training and Testing the Agent
Task 6: Display the Agent’s Policy
Task 7: Train the Agent on InvertedPendulum
Task 8: Train the Agent on Walker2d
Task 9: Plot the Learning Curve
Congratulations!
Relevant Courses
Use the following content to review prerequisites or explore specific concepts in detail.