"DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills" (https://xbpeng.github.io/projects/DeepMimic/index.html).
To train a DeepMimic model, use the following command:
python mimickit/run.py --mode train --num_envs 4096 --engine_config data/engines/isaac_gym_engine.yaml --env_config data/envs/deepmimic_humanoid_env.yaml --agent_config data/agents/deepmimic_humanoid_ppo_agent.yaml --visualize false --out_dir output/
To test a DeepMimic model, run the following command:
python mimickit/run.py --mode test --num_envs 4 --engine_config data/engines/isaac_gym_engine.yaml --env_config data/envs/deepmimic_humanoid_env.yaml --agent_config data/agents/deepmimic_humanoid_ppo_agent.yaml --visualize true --model_file data/models/deepmimic_humanoid_spinkick_model.pt
The motion data used to train the controller can be specified through motion_file in data/envs/deepmimic_humanoid_env.yaml. The default configuration trains a controller to imitate a single motion clip. To train a more general controller that can imitate different motion clips, motion_file can be used to specify a dataset file, located in data/datasets/, which will train a controller to imitate multiple motion clips.
@article{
2018-TOG-deepMimic,
author = {Peng, Xue Bin and Abbeel, Pieter and Levine, Sergey and van de Panne, Michiel},
title = {DeepMimic: Example-guided Deep Reinforcement Learning of Physics-based Character Skills},
journal = {ACM Trans. Graph.},
issue_date = {August 2018},
volume = {37},
number = {4},
month = jul,
year = {2018},
issn = {0730-0301},
pages = {143:1--143:14},
articleno = {143},
numpages = {14},
url = {http://doi.acm.org/10.1145/3197517.3201311},
doi = {10.1145/3197517.3201311},
acmid = {3201311},
publisher = {ACM},
address = {New York, NY, USA},
keywords = {motion control, physics-based character animation, reinforcement learning},
}