Open Loskiz opened 3 months ago
Hi Loskiz, I succeed in playing trained high-level teacher policy. Did you add the same arguments after _python playmultistate.py command as training? Btw, how is the performance of your trained teacher policy?
This is how I trained the policy:
python train_multistate.py --rl_device "cuda:0" --sim_device "cuda:0" --timesteps 500 --headless --task B1Z1PickMulti --experiment_dir b1-pick-multi-teacher-test --wandb --wandb_project "b1-pick-multi-teacher" --wandb_name "some descriptions" --roboinfo --observe_gait_commands --small_value_set_zero --rand_control --stop_pick
This is how I played the trained policy:
python play_multistate.py --task B1Z1PickMulti --checkpoint "/home/letianz/Projects/visual_wholebody/high-level/b1-pick-multi-teacher-test/some descriptions/checkpoints/agent_500.pt" --roboinfo --observe_gait_commands --small_value_set_zero --rand_control --stop_pick
I have not trained the policy with that much timesteps yet, since I wanted to test out the visualization first.
It looks good on your commands, maybe something wrong happened in your environment. I set up the environment as the instruction on 2x Ubuntu 20.04 PCs and both work fine. Maybe you can wait for a reply from the repo developer or try reinstalling the environment.
@Chu4nQ1n out of curiosity, did you have to toggle enableDebugVis
and enableCamera
to True to enable the visualization? Also, I am using a dual GPU system, but I'm not sure it relates to the issue.
@Chu4nQ1n out of curiosity, did you have to toggle
enableDebugVis
andenableCamera
to True to enable the visualization? Also, I am using a dual GPU system, but I'm not sure it relates to the issue.
No, I didn’t change anything. Regarding the GPU, I’m working on single 4070.
Hi Loskiz, I succeed in playing trained high-level teacher policy. Did you add the same arguments after _python playmultistate.py command as training? Btw, how is the performance of your trained teacher policy?
Hi, @Chu4nQ1n , I’m trying to reproduce the results of the teacher policy. How has your trained teacher policy performed? Despite numerous attempts, mine still performs poorly. 😢
Hi Loskiz, I succeed in playing trained high-level teacher policy. Did you add the same arguments after _python playmultistate.py command as training? Btw, how is the performance of your trained teacher policy?
Hi, @Chu4nQ1n , I’m trying to reproduce the results of the teacher policy. How has your trained teacher policy performed? Despite numerous attempts, mine still performs poorly. 😢
Same. I also tried to remove some objects during training. In this case, the success rate increased steady, but 60,000 timesteps still did not converge to the optimum.
Hi all, for the display problem, have you checked if you can successfully run and render isaacgym's official example?
For the high-level learning problem, I just have a quick check, the high-level part is clean, and the problem comes from some recent change of the low-level part, as I can learn well with my previous low-level model. I am working on that, and will let you know soon once I fix it.
Hi all, for the display problem, have you checked if you can successfully run and render isaacgym's official example?
Yes. I have run several Issac Gym's examples with visualization, and the low-level visualization, too, with no problem. It seems that not being able to play the high level visualization is the only issue I am having right now.
I am using a setup with dual RTX 4090 and a Threadripper CPU. I did run into some problems with the way the dual GPUs interact with the training code on the low-level. Do you think that might be the issue here?
Hi all, for the display problem, have you checked if you can successfully run and render isaacgym's official example?
Yes. I have run several Issac Gym's examples with visualization, and the low-level visualization, too, with no problem. It seems that not being able to play the high level visualization is the only issue I am having right now.
I am using a setup with dual RTX 4090 and a Threadripper CPU. I did run into some problems with the way the dual GPUs interact with the training code on the low-level. Do you think that might be the issue here?
Hi @Loskiz , have you tried to set CUDA_VISIBLE_DEVICES=0
when you use a multi-GPU device? My multi-GPU device works well when I play the trained high-level policy, using the command,
cd high-level
CUDA_VISIBLE_DEVICES=0 \
python play_multistate.py \
--checkpoint /path/to/agent_60000.pt \
--rl_device "cuda:0" \
--sim_device "cuda:0" \
--timesteps 60000 \
--task B1Z1PickMulti \
--experiment_dir b1-pick-multi-teacher \
--roboinfo \
--observe_gait_commands \
--small_value_set_zero \
--rand_control \
--stop_pick \
--graphics_device_id 0
cd ..
Regarding the line of code responsible for creating the simulation environment, https://github.com/Ericonaldo/visual_wholebody/blob/7243de41b2a8d5659855d2178b519e232a27bf23/high-level/envs/b1z1_base.py#L407 I suggest a slight modification that could resolve a bug encountered during the training of high-level policy on multi-GPU devices.
self.sim = super().create_sim(self.sim_id, self.graphics_device_id, self.physics_engine, self.sim_params)
Btw have you succeeded in reproducing the results of high-level policy yet?
@hatimwen Refer to my answer in this issue.
Hello,
When I'm trying to play the trained high-level teacher policy, I can see the process running in terminal, but the Isaac Gym window is always a black screen. I have trained and loaded the low-level policy, and playing the low-level policy shows a visualization as normal.
In the
b1z1_pickmulti.yaml
file underhigh-level/data/cfg
, I toggledenableDebugVis
toTrue
, but the windows is still black. When I toggled bothenableDebugVis
andenableCamera
toTrue
, it produces the following error message:I am wondering if the black screen when playing the high-level teacher policy is a expected behavior, and if so, is there a way to enable the visualization? Thank you.