Reinforcement Learning

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

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 …

Presentation on applying Reinforcement Learning directly on real robots

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

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 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 …

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.

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

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

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