AWR - Advantage-Weighted Regression

"Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning" (https://xbpeng.github.io/projects/AWR/index.html).

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To train an AWR 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_awr_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_awr_agent.yaml --visualize true --model_file data/models/deepmimic_humanoid_awr_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.

Citation

@article{
	AWRPeng19,
	author = {Xue Bin Peng and Aviral Kumar and Grace Zhang and Sergey Levine},
	title = {Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning},
	journal = {CoRR},
	volume = {abs/1910.00177},
	year = {2019},
	url = {https://arxiv.org/abs/1910.00177},
	archivePrefix = {arXiv},
	eprint = {1910.00177},
	timestamp = {Tue, 01 October 2019 11:27:50 +0200},
	bibsource = {dblp computer science bibliography, https://dblp.org}
}