AGiannoutsos/car_racer_gym
Apply major Reinforcement Learning algorithms (DQN,PPO,A2C) to CarRacing-v0 from GymAI environment.
This project helps evaluate different Reinforcement Learning (RL) algorithms for autonomous driving tasks in simulated environments. It takes various RL algorithms like DQN, PPO, and A2C and applies them to the CarRacing-v0 problem. The output includes performance videos (GIFs) and detailed experiment reports. This would be used by students or researchers studying and comparing RL algorithms for control problems.
No commits in the last 6 months.
Use this if you are a student or researcher in reinforcement learning looking to understand and compare the performance of DQN, PPO, and A2C algorithms on a classic simulated car racing problem.
Not ideal if you are looking for a ready-to-deploy autonomous driving solution for real-world scenarios or a platform to design custom RL environments from scratch.
Stars
28
Forks
8
Language
Jupyter Notebook
License
—
Category
Last pushed
Jan 04, 2022
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/AGiannoutsos/car_racer_gym"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
LucasAlegre/sumo-rl
Reinforcement Learning environments for Traffic Signal Control with SUMO. Compatible with...
hilo-mpc/hilo-mpc
HILO-MPC is a Python toolbox for easy, flexible and fast development of...
reiniscimurs/DRL-robot-navigation
Deep Reinforcement Learning for mobile robot navigation in ROS Gazebo simulator. Using Twin...
kyegomez/RoboCAT
Implementation of Deepmind's RoboCat: "Self-Improving Foundation Agent for Robotic Manipulation"...
cbfinn/gps
Guided Policy Search