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HighwayEnv Alternatives
Similar projects and alternatives to HighwayEnv
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deepdrive-zero
Top down 2D self-driving car simulator built for running experiments in minutes, not weeks
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gym-pybullet-drones
PyBullet Gymnasium environments for single and multi-agent reinforcement learning of quadcopter control
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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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.
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better HighwayEnv alternative or higher similarity.
HighwayEnv reviews and mentions
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
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A note from our sponsor - WorkOS
workos.com | 28 Apr 2024
Stats
Basic HighwayEnv repo stats
3
2,356
7.6
14 days ago
Farama-Foundation/HighwayEnv is an open source project licensed under MIT License which is an OSI approved license.
The primary programming language of HighwayEnv is Python.
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