neat
[ICCV'21] NEAT: Neural Attention Fields for End-to-End Autonomous Driving (by autonomousvision)
DI-drive
Decision Intelligence Platform for Autonomous Driving simulation. (by opendilab)
neat | DI-drive | |
---|---|---|
1 | 2 | |
293 | 521 | |
2.0% | -16.1% | |
0.0 | 0.0 | |
over 1 year ago | over 1 year ago | |
Python | Python | |
MIT License | Apache License 2.0 |
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.
neat
Posts with mentions or reviews of neat.
We have used some of these posts to build our list of alternatives
and similar projects.
DI-drive
Posts with mentions or reviews of DI-drive.
We have used some of these posts to build our list of alternatives
and similar projects.
- Try simple interfaces and customized driving policy and casezoo set on DI-driveļ¼
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Is reinforcement learning being used for the development of self-driving cars?
Some attempts on driving simulators have achieved good results(eg. DI-drive, DI-drive is an open-source application platform under OpenDILab. DI-drive applies different simulator/datasets/cases in Decision Intelligence Training & Testing for Autonomous Driving Policy). The basic idea mainly includes initializing with imitation learning, and then using reinforcement learning to obtain results that surpass expert data after reaching a certain performance. Some use the perceptual Label to train the backbone of the network, then freeze the backbone, and use reinforcement learning to specifically train the affordance method from perceptual embedding to action output. Others use a multi-model fusion approach, in which the model trained by reinforcement learning is used together with other methods to obtain the driving output. However, the emulator-based method is mainly end-to-end, and its security is difficult to guarantee, and it is difficult to apply to real vehicle scenarios.
What are some alternatives?
When comparing neat and DI-drive you can also consider the following projects:
tianshou - An elegant PyTorch deep reinforcement learning library.
mini-AlphaStar - (JAIR'2022) A mini-scale reproduction code of the AlphaStar program. Note: the original AlphaStar is the AI proposed by DeepMind to play StarCraft II. JAIR = Journal of Artificial Intelligence Research.
imitation - Clean PyTorch implementations of imitation and reward learning algorithms
restyle-encoder - Official Implementation for "ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement" (ICCV 2021) https://arxiv.org/abs/2104.02699
eirli - An Empirical Investigation of Representation Learning for Imitation (EIRLI), NeurIPS'21