CURL: Contrastive Unsupervised Representation Learning for Sample-Efficient Reinforcement Learning

Installation

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

Instructions

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: 29000 | 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

The maximum score for cartpole swing up is around 845 pts. Notice how CURL solves visual cartpole in 30k steps! This takes about and hour of training depending on your GPU. For reference, the state-state-of-the-art end-to-end method D4PG takes 50,000,000 timesteps to solve the same problem. CURL is ~1000x more efficient!

The above output decodes as:

train - training episode
E - total number of episodes 
S - total number of environment steps
D - duration in seconds to train 1 episode
R - 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.