Update Readme

README.md

@@ -1,25 +1,3 @@
-# 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:
 ```
@@ -31,7 +9,7 @@ source activate pytorch_sac_ae
 ```
 
 ## Instructions
-To train an SAC+AE agent on the `cheetah run` task from image-based observations  run:
+To train an agent on the `cheetah run` task from image-based observations  run:
 ```
 python train.py \
     --domain_name cheetah \
@@ -42,7 +20,9 @@ python train.py \
     --save_video \
     --save_tb \
     --work_dir ./log \
-    --seed 1
+    --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:
 ```
@@ -71,9 +51,3 @@ 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](results/graph.png)