Comments (2)
- We didn't spend much time optimizing for computation speed. The CADRL policy in particular uses quite outdated custom NN implementations that likely contribute to slow runtime. If you have access to multiple machines or could run on a bunch of cloud/AWS machines in parallel, that would be one way to generate more samples faster.
- Changing this line to use your gpu should do it. In my experience, using CPU led to a higher number of samples per second, maybe because there were no images or conv layers in the NN architecture. But, I would be interested to hear about your experience on this.
from gym-collision-avoidance.
Thank you very much for your answer, Mr. Everet.
from gym-collision-avoidance.
Related Issues (18)
- Code request HOT 2
- Loading weight problem HOT 2
- Training source HOT 2
- env.step(action) list size HOT 5
- Could you please tell me where are the codes calculating the diffusion map? thanks. HOT 3
- ModuleNotFoundError: No module named 'rvo2' HOT 6
- is the function world_coordinates_to_map_indices in Map is right? HOT 1
- TypeError: 'NoneType' object is not subscriptable HOT 1
- Problem with pip during installation HOT 5
- Error during executio initial example HOT 3
- AssertionError in while performing Minimum working example HOT 1
- The PPOCADRLPolicy could not load the mfe_network? HOT 3
- Visual settings about the color and size of the agent, etc. HOT 6
- ModuleNotFoundError: No module named 'scipy' when running example.sh HOT 2
- Agents' abnormal behaviors HOT 5
- How Can I Import Customized Map? HOT 4
- Train new policy from scratch HOT 3
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