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examples.txt
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### Below are commands for reproducing MELD results presented in the paper
### cheetahVel
CUDA_VISIBLE_DEVICES=0 python meld/scripts/run_meld.py \
--root_dir save_dir \
--experiment_name dense_cheetah \
--gin_file meld/configs/0_meld_dense.gin \
--gin_file meld/configs/1_cheetahVel.gin \
--gin_file meld/configs/2_cheetahVel_1episode.gin
### Sawyer Reacher
CUDA_VISIBLE_DEVICES=0 python meld/scripts/run_meld.py \
--root_dir save_dir \
--experiment_name dense_sawyerReacher \
--gin_file meld/configs/0_meld_dense.gin \
--gin_file meld/configs/1_sawyerReacher.gin \
--gin_file meld/configs/2_sawyerReacher_1episode.gin
### Sawyer Shelf
CUDA_VISIBLE_DEVICES=0 python meld/scripts/run_meld.py \
--root_dir save_dir \
--experiment_name dense_sawyerShelf \
--gin_file meld/configs/0_meld_dense.gin \
--gin_file meld/configs/1_sawyerShelf.gin \
--gin_file meld/configs/2_sawyerShelf_1episode.gin
### Sawyer Peg
CUDA_VISIBLE_DEVICES=0 python meld/scripts/run_meld.py \
--root_dir save_dir \
--experiment_name dense_sawyerButton \
--gin_file meld/configs/0_meld_dense.gin \
--gin_file meld/configs/1_sawyerPeg.gin \
--gin_file meld/configs/2_sawyerPeg_1episode.gin
### Sawyer Button
CUDA_VISIBLE_DEVICES=0 python meld/scripts/run_meld.py \
--root_dir save_dir \
--experiment_name dense_sawyerButtons \
--gin_file meld/configs/0_meld_dense.gin \
--gin_file meld/configs/1_sawyerButtons.gin \
--gin_file meld/configs/2_sawyerButtons_1episode.gin \
--gin_param train_eval.rb_checkpoint_freq_in_iter=300
### Sawyer Button, sparse reward test-time
# Please first run the dense reward experiment above till converged (~200k global steps / ~100k env steps)
# then replace <DIR_TO_REPLAY_BUFFER> below with the saved replay_buffers directory
CUDA_VISIBLE_DEVICES=0 python meld/scripts/run_meld.py \
--root_dir save_dir \
--experiment_name sparse_sawyerButtons \
--gin_file meld/configs/0_meld_sparse.gin \
--gin_file meld/configs/1_sawyerButtons.gin \
--gin_file meld/configs/2_sawyerButtons_2episodes_sparse.gin \
--gin_param train_eval.offline_episode_len=40 \
--gin_param train_eval.offline_data_dir=\"<DIR_TO_REPLAY_BUFFER>\"