## 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 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 \
    --img_source video \
    --resource_files "/media/vedant/cpsDataStorageWK/Vedant/train/*.mp4"
```
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).