House3D_RoomNav_baseline

This is the baseline model of the RoomNav task using House3D that I implemented.

It implements A3C with gated-LSTM policy for discrete actions.

In the paper, they used 120 or 200 processes but I only used 20 processes. https://arxiv.org/abs/1801.02209

requirements

• python 3.6+
• pytorch 0.4.1

pip install -r requirements.txt 

you should input your path to config.json

and your gpu ids depending on your environment in Class Params() in main.py . (In my case, I used gpu 0 for tests and gpu 1, 2, 3 for training)

Training

python main.py 

Success rate

Each line represents each env used in the learning.

Todo

• add batch structure 64(batch)*30(continuous actions)
• add decay the learning rate by a factor of 1.5 when the difference of KL-divergence becomes larger than 0.01

Project Reference

https://github.com/dgriff777/rl_a3c_pytorch