HighwayEnv
A minimalist environment for decision-making in autonomous driving (by Farama-Foundation)
deepdrive-zero
Top down 2D self-driving car simulator built for running experiments in minutes, not weeks (by deepdrive)
| HighwayEnv | deepdrive-zero | |
|---|---|---|
| 3 | 1 | |
| 2,365 | 32 | |
| 2.0% | - | |
| 7.5 | 0.0 | |
| 7 days ago | over 2 years ago | |
| Python | Python | |
| MIT License | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
HighwayEnv
Posts with mentions or reviews of HighwayEnv.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-02-19.
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Looking for a a tutorial/blog post/ codebase/ anything that deals with highway-env possibly the racetrack variant) with a DQN
More or less what the title says. I have already tried this https://github.com/Farama-Foundation/HighwayEnv/blob/master/scripts/sb3_racetracks_ppo.py, using a dqn from sb3 instead of the ppo but the results weren't good, i'm open to any suggestion.
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RecurrentPPO (SB3-contrib) learning for autonomous driving
Hi everyone! I'm a complete newbie to DRL, so please forgive my lack of understanding of some things on here. I'm training a recPPO from SB3-contrib on E.Leurent's Highway env [https://github.com/eleurent/highway-env] (I customized the action to be more high-level). During training I get the desired behavioural outcome from the agent but I noticed that some training metrics of the model seem quite off respect to the trend found online (especially the explained variance). I just wanted an opinion from some more navigated fellas in here! Can I somehow fix this trend by hyperparameter tuning or do I have e.g. to modify the reward function somehow? How can I improve the training? For any details I'm always available. I share the tensorboard plots obtained for RecPPO.
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Low Graphics Consuming Simulators for Self-Driving Cars
This is an excellent one https://github.com/eleurent/highway-env
deepdrive-zero
Posts with mentions or reviews of deepdrive-zero.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-02-19.
-
Low Graphics Consuming Simulators for Self-Driving Cars
Also, here's one I made for my own experiments https://github.com/deepdrive/deepdrive-zero
What are some alternatives?
When comparing HighwayEnv and deepdrive-zero you can also consider the following projects:
gym-pybullet-drones - PyBullet Gymnasium environments for single and multi-agent reinforcement learning of quadcopter control
MuJoCo_RL_UR5 - A MuJoCo/Gym environment for robot control using Reinforcement Learning. The task of agents in this environment is pixel-wise prediction of grasp success chances.
gym-md - MiniDungeons for OpenAI Gym
rocket-league-gym - A Gym-like environment for Reinforcement Learning in Rocket League
PythonRobotics - Python sample codes for robotics algorithms.
multirotor - Multicopter UAV simulation for control/RL experiments.