Combining Evolutionary Algorithms and deep RL in various ways
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.gitignore
ES.py
GA.py
Optimizers.py
README.md
ddpg.py
distributed.py
es_grad.py
es_grad_hard.py
es_grad_im.py
memory.py
models.py
random_process.py
samplers.py
td3.py
util.py

README.md

CEM-RL

Pytorch implementation of CEM-RL: https://arxiv.org/pdf/1810.01222.pdf

To reproduce the results of the paper:

Without importance mixing:

python es_grad.py --env ENV_NAME --use_td3 --output OUTPUT_FOLDER

With importance mixing:

python es_grad_im.py --env ENV_NAME --use_td3 --output OUTPUT_FOLDER

TD3:

python distributed.py --env ENV_NAME --use_td3 --output OUTPUT_FOLDER