This repository is the official implementation of CURL for the DeepMind control experiments. Atari experiments were done in a separate codebase available here. Our implementation of SAC is based on SAC AE by Denis Yarats.
All of the dependencies are in the conda_env.yml
file. They can be installed manually or with the following command:
conda env create -f conda_env.yml
To train a CURL agent on the cartpole swingup
task from image-based observations run bash script/run.sh
from the root of this directory. The run.sh
file contains the following command, which you can modify to try different environments / hyperparamters.
CUDA_VISIBLE_DEVICES=0 python train.py \
--domain_name cartpole \
--task_name swingup \
--encoder_type pixel \
--action_repeat 8 \
--save_tb --pre_transform_image_size 100 --image_size 84 \
--work_dir ./tmp \
--agent curl_sac --frame_stack 3 \
--seed -1 --critic_lr 1e-3 --actor_lr 1e-3 --eval_freq 10000 --batch_size 128 --num_train_steps 1000000
In your console, you should see printouts that look like:
| train | E: 221 | S: 28000 | D: 18.1 s | R: 785.2634 | BR: 3.8815 | A_LOSS: -305.7328 | CR_LOSS: 190.9854 | CU_LOSS: 0.0000
| train | E: 225 | S: 28500 | D: 18.6 s | R: 832.4937 | BR: 3.9644 | A_LOSS: -308.7789 | CR_LOSS: 126.0638 | CU_LOSS: 0.0000
| train | E: 229 | S: 29500 | D: 18.8 s | R: 683.6702 | BR: 3.7384 | A_LOSS: -311.3941 | CR_LOSS: 140.2573 | CU_LOSS: 0.0000
| train | E: 233 | S: 29500 | D: 19.6 s | R: 838.0947 | BR: 3.7254 | A_LOSS: -316.9415 | CR_LOSS: 136.5304 | CU_LOSS: 0.0000
For reference, the maximum score for cartpole swing up is around 845 pts, so CURL has converged to the optimal score. This takes about an hour of training depending on your GPU.
Log abbreviation mapping:
train - training episode
E - total number of episodes
S - total number of environment steps
D - duration in seconds to train 1 episode
R - mean episode reward
BR - average reward of sampled batch
A_LOSS - average loss of actor
CR_LOSS - average loss of critic
CU_LOSS - average loss of the CURL encoder
All data related to the run is stored in the specified working_dir
. To enable model or video saving, use the --save_model
or --save_video
flags. For all available flags, inspect train.py
. To visualize progress with tensorboard run:
tensorboard --logdir log --port 6006
and go to localhost:6006
in your browser. If you're running headlessly, try port forwarding with ssh.
For GPU accelerated rendering, make sure EGL is installed on your machine and set export MUJOCO_GL=egl
. For environment troubleshooting issues, see the DeepMind control documentation.