This repository is the official implementation of DY2P. Our implementation is based on SAC+AE.
Required dependencies of this repo can be installed by running:
conda env create -f environment.yml Then you can activate the environment by running:
source activate py3.6 To train a DY2P agent on the cartpole swingup task from images, you can run:
python train.py \
--domain_name cartpole \
--task_name swingup \
--action_repeat 8 \
--save_tb \
--seed 0 \
--eval_freq 1250 \
--batch_size 512 \
--pre_transform_image_size 84 \
--image_size 84 \
--cody_lr 1e-4 \
--results_dir ./logs \
--time_step 2 \
--omega_cody_loss 0.01 \
--fc_output_logits True \
--kl_use_target True \
or you can run the script for all six tasks:
bash train.sh
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