Creează un cont pentru a te abona la acest blog și a-i urmări articolele pe pagina Lectură.
-
Making RL Work Out-of-the-Box (WIP)
-
Direct Policy Search Tutorial - RL Summer School 2026
-
DQN Tutorial - RL Summer School 2026
A blog post of this tutorial can be found here: https://araffin.github.io/post/rl102/
-
Exploration in Continuous Control RL
-
Recent Advances in RL for Continuous Control (SOTA) - Early 2026 Update
-
RL103: From Deep Q-Learning (DQN) to Soft Actor-Critic (SAC) and Beyond
This second blog post continues my practical introduction to (deep) reinforcement learning, presenting the main concepts and providing intuitions to understand the more recent Deep RL algorithms.
-
Stable-Baselines3 (SB3) Tutorial: Getting Started With Reinforcement Learning
-
PhD Defense: Enabling Reinforcement Learning on Real Robots
-
Designing (Robot) Software That Is Easy to Use
-
RL102: From Tabular Q-Learning to Deep Q-Learning (DQN)
This blog post is meant to be a practical introduction to (deep) reinforcement learning1, presenting the main concepts and providing intuitions to understand the more recent Deep RL algorithms.
-
Getting SAC to Work on a Massive Parallel Simulator: Tuning for Speed (Part II)
This second post details how I tuned the Soft-Actor Critic (SAC) algorithm to learn as fast as PPO in the context of a massively parallel simulator (thousands of robots simulated in parallel).
-
Recent Advances in RL for Continuous Control
-
Automatic Hyperparameter Tuning - In Practice (Part 2)
This is the second (and last) post on automatic hyperparameter optimization.
-
Getting SAC to Work on a Massive Parallel Simulator: An RL Journey With Off-Policy Algorithms (Part I)
This post details how I managed to get the Soft-Actor Critic (SAC) and other off-policy reinforcement learning algorithms to work on massively parallel simulators (think Isaac Sim with thousands of robots simulated in parallel).
-
Enabling Reinforcement Learning on Real Robots
-
Ingredients for Learning Locomotion Directly on Real Hardware
-
Designing and Running Real-World RL Experiments
This talk was also given at the RL Symposium organized by Boston University on May 10, 2024.
-
Practical Tips for Reliable Reinforcement Learning
This talk was also given at the Industrial RL Workshop organized in Paris Saclay in November 2023.
-
An Open-Loop Baseline for Reinforcement Learning Locomotion Tasks
-
Knowledge Guided Reinforcement Learning for Robotics
-
DQN Tutorial
A blog post of this talk can be found here: https://araffin.github.io/post/rl102/
-
Automatic Hyperparameter Tuning - A Visual Guide (Part 1)
When you’re building a machine learning model, you want to find the best hyperparameters to make it shine.
-
SBX: Stable Baselines Jax
Proof of concept version of Stable-Baselines3 in Jax.
-
Datasaurust
Blazingly fast implementation of the Datasaurus paper (500x faster than the original): “Same Stats, Different Graphs: Generating Datasets with Varied Appearance and Identical Statistics through Simulated Annealing” by Justin Matejka and George Fitzmaurice.
-
Learning to Exploit Elastic Actuators for Quadruped Locomotion
-
Training RL agents directly on real robots
-
Tutorial: Tools for Robotic Reinforcement Learning
-
The 37 Implementation Details of Proximal Policy Optimization
-
Stable-Baselines3: Reliable Reinforcement Learning Implementations
-
Rliable: Better Evaluation for Reinforcement Learning - A Visual Explanation
It is critical for Reinforcement Learning (RL) practitioners to properly evaluate and compare results.
-
Smooth Exploration for Robotic Reinforcement Learning
-
Learning to Race in Hours with Reinforcement Learning
-
Fault-Tolerant Six-DoF Pose Estimation for Tendon-Driven Continuum Mechanisms
-
RL Tips and Tricks / The Challenges of Applying RL to Real Robots
RL Tips and Tricks Slides SB3 Hands-on Session slides SB3 Hands-on Session github repo
-
Stable-Baselines3: Reliable Reinforcement Learning Implementations
After several months of beta, we are happy to announce the release of Stable-Baselines3 (SB3) v1.0, a set of reliable implementations of reinforcement learning (RL) algorithms in PyTorch =D!
-
Stable Baselines3
Stable Baselines3 is a set of improved implementations of reinforcement learning algorithms in PyTorch.
-
RL Tutorial on Stable Baselines
-
SRL - Stable Baselines Presentation
-
Learning to Drive Smoothly in Minutes
Learning to drive smoothly in minutes, using a reinforcement learning algorithm – Soft Actor-Critic (SAC) – and a Variational AutoEncoder (VAE) in the Donkey Car simulator.
-
Learning to Drive Smoothly in Minutes
Read the full article on Medium
-
Decoupling feature extraction from policy learning: assessing benefits of state representation learning in goal based robotics
-
RL Baselines Zoo
A collection of trained Reinforcement Learning (RL) agents, with tuned hyperparameters, using Stable Baselines.
-
S-RL Toolbox
S-RL Toolbox: Reinforcement Learning (RL) and State Representation Learning (SRL) Toolbox for Robotics.
-
Stable Baselines
Stable Baselines is a set of improved implementations of Reinforcement Learning (RL) algorithms based on OpenAI Baselines.
-
S-RL Toolbox: Environments, Datasets and Evaluation Metrics for State Representation Learning
-
PythonRobotics: a Python code collection of robotics algorithms
-
Stable Baselines: a Fork of OpenAI Baselines — Reinforcement Learning Made Easy
Read the full article on Medium
-
Simple and Robust {Computer — Arduino} Serial Communication
Read the full article on Medium
-
Autonomous Racing Robot With an Arduino, a Raspberry Pi and a Pi Camera
Read the full article on Medium
-
Racing Robot
Autonomous toy racing car.