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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 -e habitat-lab
pip install -e habitat-baselines

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: The PPO implementation is based on pytorch-a2c-ppo-acktr.

dependencies: A recent version of pytorch, 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-lab/, extraction will create a data folder at habitat-lab/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

You can reduce training time by changing the trainer from the default implement to VER by setting trainer_name to "ver" in either the config or via the command line.

python -u habitat_baselines/run.py \
  --exp-config habitat_baselines/config/pointnav/ppo_pointnav_example.yaml \
  --run-type train \
  habitat_baselines.trainer_name=ver

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, and Depth PPO models for MatterPort3D and Gibson. 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 hyperparameters for large scale training and loads the Gibson PointGoal Navigation Dataset instead of the test scenes. Change the /benchmark/nav/pointnav: pointnav_gibson in habitat_baselines/config/pointnav/ppo_pointnav.yaml to /benchmark/nav/pointnav: pointnav_mp3d in the defaults list 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: