BrunoSAC
Exchangeable Models in Meta Reinforcement Learning
I. Korshunova, J. Degrave, J. Dambre, A. Gretton, F. Huszár
Lifelong Learning Workshop at ICML 2020
Requirements
The code was used with the following settings:
- python3
- tensorflow-gpu==1.14.0
- tensorflow-probability==0.7.0
- gym==0.17.1
- mujoco-py== 2.0.2.9
- mujoco200
Training and testing
To train and then test BrunoSAC on Cheetah-Dir run:
python meta_cheetah_dir.py --train
python meta_cheetah_dir.py --test
Similarly, for the oracle:
python meta_cheetah_dir.py --train --oracle
python meta_cheetah_dir.py --test --oracle
To plot the learning curves and test rewards:
python -m plots.plot_train_cheetah_dir
python -m plots.plot_test_cheetah_dir
The same commands can be used with meta_cheetah_vel.py for the Cheetah-Vel experiments.
Questions?
Please send an email to irene.korshunova@gmail.com, and I'll be happy to answer.