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The policy does not learn in the unicycle mode #18

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ehsankf opened this issue Jun 22, 2024 · 1 comment
Open

The policy does not learn in the unicycle mode #18

ehsankf opened this issue Jun 22, 2024 · 1 comment

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@ehsankf
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ehsankf commented Jun 22, 2024

Hi,

I trained the model with the unicycle kinematic and using 20 humans as obstacles and I noticed that the policy does not learn. Is there any requirement for the number of humans with unicycle kinematic?
I believe the interval (-0.06, 0.06) for the change of angle i.e., \Delta \theta is too small for the agent to navigate this dense environment.

Thanks.

@Shuijing725
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If you have a strong computer, you can increase the number of parallel environments by changing args.num_processes and the number of training steps args.num_env_steps in arguments.py . Otherwise, you can reduce the task difficulty by fixing the starting and goal positions of robot and/or humans.

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