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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.

Teaching a Robot to Walk Using Deep Reinforcement Learning

You will learn to:

Perform deep reinforcement learning.

Train and test a REINFORCE agent.

Work with the Gymnasium API.

Work with the MuJoCo environments.


Machine Learning

Deep Learning

Reinforcement Learning



Programming in Python

Basic knowledge of deep learning

Building and training a neural network in PyTorch




Gymnasium logo



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.

The Walker2d environment
The Walker2d environment

Project Tasks



Task 0: Introduction


Exploring the MuJoCo Environments

Task 1: Explore the InvertedPendulum Environment

Task 2: Explore the Walker2d Environment


Building the REINFORCE Agent

Task 3: Build the Policy Network

Task 4: Implement the REINFORCE Algorithm

Task 5: Build the AgentTrainer Class


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