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View Code? Open in Web Editor NEWSupplementary code for SIGGRAPH 2021 paper: Discovering Diverse Athletic Jumping Strategies
License: MIT License
Supplementary code for SIGGRAPH 2021 paper: Discovering Diverse Athletic Jumping Strategies
License: MIT License
after 12000 times of training, run evaluate.py, the render is different from that in the video, and there is no run-up process. Is there a problem with my parameter setting?
As above.
I cannot get the result the same as the paper. When the training of jump policy, I always gets reward 0.
The default values to learn runup policy are:
algorithm.max_iterations: 2000
experiment.env: jumper_run2
env.jumper_run2.angular_v: [-3.0, -3.0, 1.0]
env.jumper_run2.linear_v_z: -2.4
The jump policy with the following parameters, which are the recommended ones to learn Fosbury Flop.
algorithm.max_iterations: 12000
experiment.env: highjump
# initial state file generated by the run-up training
env.highjump.initial_state: results/runup-2022-Feb-10-175005/checkpoint_2000.tar.npy
# wall orientation in degrees
env.highjump.wall_rotation: -0.05
# must correspond to the training height of the checkpoint
env.highjump.initial_wall_height: 0.5
Hi,
I noticed that you used a layer size of 200 for input features, but at the end you seems to use only the joints position. Is this somethings i'm missing? do you train on larger feature (like velociy) to get more motion differentiation? and then use a small portion (joints positions) that you are adding the offset aftermath?
Thank you.
Hi,
I found your paper very interesting!
I found training p-vae(train_pose_vae.py) and training ppo for motion control (train.py).
Can you tell me which part of the released code is about the bayesian diversity search?
It says in the paper that it is implemented using GPFlow, but I found no code using it.
Thank you.
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