Merge branch 'master' of github.com:kentsommer/pytorch-value-iteration-networks

Author: Kent Sommer

README.md

@@ -46,7 +46,7 @@ python train.py --datafile dataset/gridworld_28x28.npz --imsize 28 --lr 0.002 --
 - `l_q`: Number of channels in q layer (~actions) in VI-module. Default: 10, described in paper.
 - `batch_size`: Batch size. Default: 128
 
-## How to visualize / test paths (requires training first)
+## How to test / visualize paths (requires training first)
 #### 8x8 gridworld
 ```bash
 python test.py --weights trained/vin_8x8.pth --imsize 8 --k 10
@@ -59,10 +59,15 @@ python test.py --weights trained/vin_16x16.pth --imsize 16 --k 20
 ```bash
 python test.py --weights trained/vin_28x28.pth --imsize 28 --k 36
 ```
+To visualize the optimal and predicted paths simply pass:
+```bash 
+--plot
+```
 
 **Flags**: 
 - `weights`: Path to trained weights.
 - `imsize`: The size of input images. One of: [8, 16, 28]
+- `plot`: If supplied, the optimal and predicted paths will be plotted 
 - `k`: Number of Value Iterations. Recommended: [10 for 8x8, 20 for 16x16, 36 for 28x28]
 - `l_i`: Number of channels in input layer. Default: 2, i.e. obstacles image and goal image.
 - `l_h`: Number of channels in first convolutional layer. Default: 150, described in paper.
@@ -85,6 +90,13 @@ Test set | 13846 | 77203 | 251755
 
 The datasets (8x8, 16x16, and 28x28) included in this repository can be reproduced using the ```dataset/make_training_data.py``` script. Note that this script is not optimized and runs rather slowly (also uses a lot of memory :D)
 
+## Performance: Success Rate
+This is the success rate from rollouts of the learned policy in the environment (taken over 5000 randomly generated domains). 
+
+Success Rate | 8x8 | 16x16 | 28x28
+-- | -- | -- | --
+PyTorch | 99.69% | 96.99% | 91.07%
+
 ## Performance: Test Accuracy
 
 **NOTE**: This is the **accuracy on test set**. It is different from the table in the paper, which indicates the **success rate** from rollouts of the learned policy in the environment.