SAC+AE implementation in PyTorch

This is PyTorch implementation of SAC+AE from

Improving Sample Efficiency in Model-Free Reinforcement Learning from Images by

Denis Yarats, Amy Zhang, Ilya Kostrikov, Brandon Amos, Joelle Pineau, Rob Fergus.

[Paper] [Webpage]

Requirements

We assume you have access to a gpu that can run CUDA 9.2. Then, the simplest way to install all required dependencies is to create an anaconda environment by running:

conda env create -f conda_env.yml

After the instalation ends you can activate your environment with:

source activate pytorch_sac_ae

Instructions

To train an SAC+AE agent on the cheetah run task from image-based observations run:

python train.py \
    --domain_name cheetah \
    --task_name run \
    --encoder_type pixel \
    --decoder_type pixel \
    --action_repeat 4 \
    --save_video \
    --save_tb \
    --work_dir ./log \
    --seed 1

This will produce 'log' folder, where all the outputs are going to be stored including train/eval logs, tensorboard blobs, and evaluation episode videos. One can attacha tensorboard to monitor training by running:

tensorboard --logdir log

and opening up tensorboad in your browser.

The console output is also available in a form:

| train | E: 1 | S: 1000 | D: 0.8 s | R: 0.0000 | BR: 0.0000 | ALOSS: 0.0000 | CLOSS: 0.0000 | RLOSS: 0.0000

a training entry 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
ALOSS - average loss of actor
CLOSS - average loss of critic
RLOSS - average reconstruction loss (only if is trained from pixels and decoder)

while an evaluation entry:

| eval | S: 0 | ER: 21.1676

which just tells the expected reward ER evaluating current policy after S steps. Note that ER is average evaluation performance over num_eval_episodes episodes (usually 10).

Results

Our method demonstrates significantly improved performance over the baseline SAC:pixel. It matches the state-of-the-art performance of model-based algorithms, such as PlaNet (Hafner et al., 2018) and SLAC (Lee et al., 2019), as well as a model-free algorithm D4PG (Barth-Maron et al., 2018), that also learns from raw images. Our algorithm exhibits stable learning across ten random seeds and is extremely easy to implement.