PythonRobotics: a Python code collection of robotics algorithms

2 code implementations • 31 Aug 2018 • Atsushi Sakai, Daniel Ingram, Joseph Dinius, Karan Chawla, Antonin Raffin, Alexis Paques

The focus of the project is on autonomous navigation, and the goal is for beginners in robotics to understand the basic ideas behind each algorithm.

Robotics

Smooth Exploration for Robotic Reinforcement Learning

4 code implementations • 12 May 2020 • Antonin Raffin, Jens Kober, Freek Stulp

We evaluate gSDE both in simulation, on PyBullet continuous control tasks, and directly on three different real robots: a tendon-driven elastic robot, a quadruped and an RC car.

Continuous Control reinforcement-learning +1

S-RL Toolbox: Environments, Datasets and Evaluation Metrics for State Representation Learning

5 code implementations • 25 Sep 2018 • Antonin Raffin, Ashley Hill, René Traoré, Timothée Lesort, Natalia Díaz-Rodríguez, David Filliat

State representation learning aims at learning compact representations from raw observations in robotics and control applications.

reinforcement-learning Reinforcement Learning (RL) +1

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

5 code implementations • 24 Jan 2019 • Antonin Raffin, Ashley Hill, René Traoré, Timothée Lesort, Natalia Díaz-Rodríguez, David Filliat

Scaling end-to-end reinforcement learning to control real robots from vision presents a series of challenges, in particular in terms of sample efficiency.

reinforcement-learning Reinforcement Learning (RL) +1

Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement Learning

1 code implementation • 5 Feb 2024 • Shengyi Huang, Quentin Gallouédec, Florian Felten, Antonin Raffin, Rousslan Fernand Julien Dossa, Yanxiao Zhao, Ryan Sullivan, Viktor Makoviychuk, Denys Makoviichuk, Mohamad H. Danesh, Cyril Roumégous, Jiayi Weng, Chufan Chen, Md Masudur Rahman, João G. M. Araújo, Guorui Quan, Daniel Tan, Timo Klein, Rujikorn Charakorn, Mark Towers, Yann Berthelot, Kinal Mehta, Dipam Chakraborty, Arjun KG, Valentin Charraut, Chang Ye, Zichen Liu, Lucas N. Alegre, Alexander Nikulin, Xiao Hu, Tianlin Liu, Jongwook Choi, Brent Yi

As a result, it is usually necessary to reproduce the experiments from scratch, which can be time-consuming and error-prone.

reinforcement-learning Reinforcement Learning (RL)

A2C is a special case of PPO

1 code implementation • 18 May 2022 • Shengyi Huang, Anssi Kanervisto, Antonin Raffin, Weixun Wang, Santiago Ontañón, Rousslan Fernand Julien Dossa

Advantage Actor-critic (A2C) and Proximal Policy Optimization (PPO) are popular deep reinforcement learning algorithms used for game AI in recent years.

reinforcement-learning Reinforcement Learning (RL)

Making Reinforcement Learning Work on Swimmer

no code implementations • 16 Aug 2022 • Maël Franceschetti, Coline Lacoux, Ryan Ohouens, Antonin Raffin, Olivier Sigaud

A lot of these papers report poor performance on SWIMMER from RL methods and much better performance from direct policy search methods.

reinforcement-learning Reinforcement Learning (RL)

Learning to Exploit Elastic Actuators for Quadruped Locomotion

no code implementations • 15 Sep 2022 • Antonin Raffin, Daniel Seidel, Jens Kober, Alin Albu-Schäffer, João Silvério, Freek Stulp

Spring-based actuators in legged locomotion provide energy-efficiency and improved performance, but increase the difficulty of controller design.