A3Thor - object driven navigation in indoor scene using A3C
This is final project of final semester in robotic - INT 3409 1
We using A3C - Asynchronous advantage actor critic algorithm to train an agent navigating in side simulated environment ai2thor
Overview
This project includes implementations of A3C in ./A3C/a3c.py
to train the model, using:
python main.py --is_ai2thor
to visualize result, using:
python --is_ai2thor --critic_path */A3C/model/critic-model* --actor_path */A3C/model/actor-model*
The default training parameter is 5000 episodes, 5 threads
Installation
Clone this repository:
Install Python dependencies:
pip install -r requirements.txt
Highly recommend to install tensorflow using conda:
conda install tensorflow-gpu
Project description
- this project using gym-style interface of ai2thor environment
- objective is simply picking an apple in kitchen environment - FloorPlan28
- observation space is first-view RGB 128x128 image from agent's camera
- maximum step in this project is 500
- reward fuction:
- -0.01 each time step
- 1 if agent can pick an apple, the env than terminate
- 0.01 if agent saw an apple (has been removed in latest code)
- a pre-train mobilenet-v2 model on image-net is used an feature extractor for later dense layer both actor and critic model
- actor optimizer using Advantages + Entropy term to encourage exploration (https://arxiv.org/abs/1602.01783)
Training
- this project trained on xenon E5-2667v2 + GTX1070, with 1,444,234 parameters for actor and 1,443,073 params for critic model
the objective is simple so that the model converge very fast, detail log and trained model in
./A3C/
This project greatly thanks to material: