Reinforcement Learning

DQN Tutorial

From tabular Q-learning to Deep Q-Network (DQN)

Learning to Exploit Elastic Actuators for Quadruped Locomotion

Spring-based actuators in legged locomotion provide energy-efficiency and improved performance, but increase the difficulty of controller design. Whereas previous works have focused on extensive modeling and simulation to find optimal controllers for …

Training RL agents directly on real robots

Presentation on applying Reinforcement Learning directly on real robots

Tutorial: Tools for Robotic Reinforcement Learning

Hands-on RL for Robotics with EAGERx and Stable-Baselines3

The 37 Implementation Details of Proximal Policy Optimization

Proximal policy optimization (PPO) has become one of the most popular deep reinforcement learning (DRL) algorithms. Yet, reproducing the PPO's results has been challenging in the community. While recent works conducted ablation studies to provide …

Stable-Baselines3: Reliable Reinforcement Learning Implementations

Stable-Baselines3 provides open-source implementations of deep reinforcement learning (RL) algorithms in Python. The implementations have been benchmarked against reference codebases, and automated unit tests cover 95% of the code. The algorithms …

Smooth Exploration for Robotic Reinforcement Learning

We extend the original state-dependent exploration (SDE) to apply deep reinforcement learning algorithms directly on real robots. The resulting method, gSDE, yields competitive results in simulation but outperforms the unstructured exploration on the real robot.

Learning to Race in Hours with Reinforcement Learning

Talk for the DonkeyCar community about learning to race in hours using reinforcement learning.

RL Tips and Tricks / The Challenges of Applying RL to Real Robots

Talk at the reinforcement learning virtual school on applying RL in practice and hands-on session with Stable-Baselines3.

Stable Baselines3

A set of improved implementations of reinforcement learning algorithms in PyTorch.