Skip to content

README.md

Latest commit

 

History

History
62 lines (42 loc) · 2.85 KB

README.md

File metadata and controls

62 lines (42 loc) · 2.85 KB

baselines

Installation

The habitat_baselines sub-package is NOT included upon installation by default. To install habitat_baselines, use the following command instead:

pip install -r requirements.txt
python setup.py develop --all

This will also install additional requirements for each sub-module in habitat_baselines/, which are specified in requirements.txt files located in the sub-module directory.

Reinforcement Learning (RL)

Proximal Policy Optimization (PPO)

paper: https://arxiv.org/abs/1707.06347

code: majority of the PPO implementation is taken from pytorch-a2c-ppo-acktr.

dependencies: pytorch 1.0, for installing refer to pytorch.org

For training on sample data please follow steps in the repository README. You should download the sample test scene data, extract it under the main repo (habitat-api/, extraction will create a data folder at habitat-api/data) and run the below training command.

train:

python -u habitat_baselines/run.py --exp-config habitat_baselines/config/pointnav/ppo_pointnav_example.yaml --run-type train

test:

python -u habitat_baselines/run.py --exp-config habitat_baselines/config/pointnav/ppo_pointnav_example.yaml --run-type eval

We also provide trained RGB, RGBD, Blind PPO models. To use them download pre-trained pytorch models from link and unzip and specify model path here.

The habitat_baselines/config/pointnav/ppo_pointnav.yaml config has better hyperparamters for large scale training and loads the Gibson PointGoal Navigation Dataset instead of the test scenes. Change the field task_config in habitat_baselines/config/pointnav/ppo_pointnav.yaml to configs/tasks/pointnav_mp3d.yaml for training on MatterPort3D PointGoal Navigation Dataset.

Classic

SLAM based

Additional Utilities

Episode iterator options: Coming very soon

Tensorboard and video generation support

Enable tensorboard by changing tensorboard_dir field in habitat_baselines/config/pointnav/ppo_pointnav.yaml.

Enable video generation for eval mode by changing video_option: tensorboard,disk (for displaying on tensorboard and for saving videos on disk, respectively)

Generated navigation episode recordings should look like this on tensorboard: