Reinforcement Learning with Augmented Data (RAD)

This is a fork from RAD [github repo] (https://github.com/MishaLaskin/rad) Official codebase for Reinforcement Learning with Augmented Data.

BibTex

@unpublished{laskin_lee2020rad,
  title={Reinforcement Learning with Augmented Data},
  author={Laskin, Michael and Lee, Kimin and Stooke, Adam and Pinto, Lerrel and Abbeel, Pieter and Srinivas, Aravind},
  note={arXiv:2004.14990}
}

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 RAD 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 / augmentations / hyperparamters.

CUDA_VISIBLE_DEVICES=0 python train.py \
    --domain_name cartpole \
    --task_name swingup \
    --encoder_type pixel --work_dir ./tmp/cartpole \
    --action_repeat 8 --num_eval_episodes 10 \
    --pre_transform_image_size 100 --image_size 84 \
    --agent rad_sac --frame_stack 3 --data_augs flip  \
    --seed 23 --critic_lr 1e-3 --actor_lr 1e-3 --eval_freq 10000 --batch_size 128 --num_train_steps 200000 &

Data Augmentations

Augmentations can be specified through the --data_augs flag. This codebase supports the augmentations specified in data_augs.py. To chain multiple data augmentation simply separate the augmentation strings with a - string. For example to apply crop -> rotate -> flip you can do the following --data_augs crop-rotate-flip.

All data augmentations can be visualized in All_Data_Augs.ipynb. You can also test the efficiency of our modules by running python data_aug.py.

Logging

In your console, you should see printouts that look like this:

| train | E: 13 | S: 2000 | D: 9.1 s | R: 48.3056 | BR: 0.8279 | A_LOSS: -3.6559 | CR_LOSS: 2.7563
| train | E: 17 | S: 2500 | D: 9.1 s | R: 146.5945 | BR: 0.9066 | A_LOSS: -5.8576 | CR_LOSS: 6.0176
| train | E: 21 | S: 3000 | D: 7.7 s | R: 138.7537 | BR: 1.0354 | A_LOSS: -7.8795 | CR_LOSS: 7.3928
| train | E: 25 | S: 3500 | D: 9.0 s | R: 181.5103 | BR: 1.0764 | A_LOSS: -10.9712 | CR_LOSS: 8.8753
| train | E: 29 | S: 4000 | D: 8.9 s | R: 240.6485 | BR: 1.2042 | A_LOSS: -13.8537 | CR_LOSS: 9.4001

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

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