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PyTorch implementation of Soft Actor-Critic + Autoencoder(SAC+AE)

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# 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](https://cs.nyu.edu/~dy1042/), [Amy Zhang](https://mila.quebec/en/person/amy-zhang/), [Ilya Kostrikov](https://github.com/ikostrikov), [Brandon Amos](http://bamos.github.io/), [Joelle Pineau](https://www.cs.mcgill.ca/~jpineau/), [Rob Fergus](https://cs.nyu.edu/~fergus/pmwiki/pmwiki.php).

[[Paper]](https://arxiv.org/abs/1910.01741) [[Webpage]](https://sites.google.com/view/sac-ae/home)

## Citation
If you use this repo in your research, please consider citing the paper as follows
```
@article{yarats2019improving,
    title={Improving Sample Efficiency in Model-Free Reinforcement Learning from Images},
    author={Denis Yarats and Amy Zhang and Ilya Kostrikov and Brandon Amos and Joelle Pineau and Rob Fergus},
    year={2019},
    eprint={1910.01741},
    archivePrefix={arXiv}
}
```

## 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.
![Results](https://raw.githubusercontent.com/denisyarats/pytorch_sac_ae/master/results/graph.png)

pytorch_sac_ae

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