A Simple Open-Loop Baseline for Reinforcement Learning Locomotion Tasks

In search of the simplest baseline capable of competing with Deep Reinforcement Learning on locomotion tasks, we propose a biologically inspired model-free open-loop strategy. Drawing upon prior knowledge and harnessing the elegance of simple …

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 …

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.

Fault-Tolerant Six-DoF Pose Estimation for Tendon-Driven Continuum Mechanisms

Fault-Tolerant 6D Pose Estimation for Soft Robot. We present a simple ensembling method to detect and handle failures on a tendon driven robot.

Decoupling feature extraction from policy learning: assessing benefits of state representation learning in goal based robotics

We evaluate the benefits of decoupling feature extraction from policy learning in robotics and propose a new way of combining state representation learning methods.

Learning to Drive Smoothly in Minutes

Learning to drive smoothly in minutes using reinforcement learning on a Donkey Car.

Learning to Drive Smoothly in Minutes

Learning to drive smoothly in minutes using reinforcement learning on a Donkey Car.

S-RL Toolbox

S-RL Toolbox: Reinforcement Learning (RL) and State Representation Learning (SRL) for Robotics