Comments (8)
when I run train_dqn_agent.py,
raise error
segmentation fault (core dumped) python train_dqn_agent.py
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Hey there! What was happening is that I was still developing that particular script but it still had some errors. It should be working now. Just do a "git pull" or clone the repo again.
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Hey there! What was happening is that I was still developing that particular script but it still had some errors. It should be working now. Just do a "git pull" or clone the repo again.
I still got the error when run train_dqn_agent.py
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That's odd. What are the specs of your machine and and which OS are you using? Does the server also crash? And if it does which error message does it display?
Also check if your CUDA is working:
"
python
import torch
torch.cuda.is_available()
"
Tell me if that is true or false
I'm curious because I have tried that script on 3 different machines with 3 different OS and it works. So I'm guessing the problem lies somewhere else
from carla-gymdrive.
That's odd. What are the specs of your machine and and which OS are you using? Does the server also crash? And if it does which error message does it display?
Also check if your CUDA is working: " python import torch torch.cuda.is_available() " Tell me if that is true or false
I'm curious because I have tried that script on 3 different machines with 3 different OS and it works. So I'm guessing the problem lies somewhere else
Thanks for your advice. I tried on another machines with same env,then it works!
By the way, There still some bugs when I run train_agent_stablebaselines3.py.
- Fisrt is the output of forward function of CustomExtractor_PPO. [return combined_features]
- Another is self.process_observations have bugs when deal with batch data.
I am trying sovle these problems, if you want, i'd like to pr my newest code that works well with sb3 ppo
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About the ppo stable baselines agent, it's not complete, that's what I'm doing at the moment. Since I changed the agent's architecture for a combination of MLPs, it's way harder to make this architecture compatible with the stable baselines, because I haven't figured out the processing that is does to the data. But rest assured, once I sort it out, i'll let you know
from carla-gymdrive.
About the ppo stable baselines agent, it's not complete, that's what I'm doing at the moment. Since I changed the agent's architecture for a combination of MLPs, it's way harder to make this architecture compatible with the stable baselines, because I haven't figured out the processing that is does to the data. But rest assured, once I sort it out, i'll let you know
https://github.com/song-hl/CARLA-GymDrive/tree/dev
I update some moudle in CustomExtractor_PPO. It works well with sb3 ppo. I hope it helps
from carla-gymdrive.
About the ppo stable baselines agent, it's not complete, that's what I'm doing at the moment. Since I changed the agent's architecture for a combination of MLPs, it's way harder to make this architecture compatible with the stable baselines, because I haven't figured out the processing that is does to the data. But rest assured, once I sort it out, i'll let you know
https://github.com/song-hl/CARLA-GymDrive/tree/dev I update some moudle in CustomExtractor_PPO. It works well with sb3 ppo. I hope it helps
Hey there! For the past few weeks I was working on another part of this project, and only in the past couple of days I worked on this particular script. Thank you so much for the help, I already pushed the new CustomExtractor with your fixed but I changed the overall architecture for a more robust one (i.e., changed the feature extractor for a better one and removed the situation variable which was only making the data more confusing for the agent. Check it out if you're interested. I added a thank you message in the Acknowledgements section of the README file crediting you for the help.
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