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Bo Zhou 4970be2bc2 | 1 year ago | |
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README.md | 1 year ago | |
actor.py | 1 year ago | |
es.py | 2 years ago | |
es_config.py | 1 year ago | |
mujoco_agent.py | 1 year ago | |
mujoco_model.py | 2 years ago | |
noise.py | 2 years ago | |
obs_filter.py | 2 years ago | |
optimizers.py | 2 years ago | |
requirements.txt | 1 year ago | |
train.py | 1 year ago | |
utils.py | 1 year ago |
Based on PARL, we have implemented the Evolution Strategies (ES) algorithm and evaluate it in Mujoco environments. Its performance reaches the same level of indicators as the paper.
Please see here to know more about Mujoco games.
To replicate the performance reported above, we encourage you to train with 24 or 48 CPUs.
If you haven't created a cluster before, enter the following command to create a cluster. For more information about the cluster, please refer to our documentation.
xparl start --port 8837 --cpu_num 24
Then we can start the distributed training by running:
python train.py
Training result will be saved in train_log
with the training curve.
PARL 是一个高性能、灵活的强化学习框架
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