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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 -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/, extraction will create a data folder at habitat/data) and run the below training command.

train:

python -u habitat_baselines/train_ppo.py \
    --use-gae \
    --sim-gpu-id 0 \
    --pth-gpu-id 0 \
    --lr 2.5e-4 \
    --clip-param 0.1 \
    --value-loss-coef 0.5 \
    --num-processes 4 \
    --num-steps 128 \
    --num-mini-batch 4 \
    --num-updates 100000 \
    --use-linear-lr-decay \
    --use-linear-clip-decay \
    --entropy-coef 0.01 \
    --log-file "train.log" \
    --log-interval 5 \
    --checkpoint-folder "data/checkpoints" \
    --checkpoint-interval 50 \
    --task-config "configs/tasks/pointnav.yaml" \

test:

python -u habitat_baselines/evaluate_ppo.py \
    --model-path "/path/to/checkpoint" \
    --sim-gpu-id 0 \
    --pth-gpu-id 0 \
    --num-processes 4 \
    --count-test-episodes 100 \
    --task-config "configs/tasks/pointnav.yaml" \

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.

Set argument --task-config to tasks/pointnav_mp3d.yaml for training on MatterPort3D point goal navigation dataset.

Classic

SLAM based

Additional Utilities

single-episode training: Algorithms can be trained with a single-episode option. This option can be used as a sanity check since good algorithms should overfit one episode relatively fast. To enable this option, add DATASET.NUM_EPISODE_SAMPLE 1 at the end of the training command, or include the single-episode yaml file in --task-config like this:

   --task-config "configs/tasks/pointnav.yaml,configs/datasets/single_episode.yaml"

tensorboard and video generation support

Enable tensorboard logging by adding --tensorboard-dir logdir when running train_ppo.py or evaluate_ppo.py

Enable video generation for evaluate_ppo.py using --video-option: specifying tensorboard(for displaying on tensorboard) or disk (for saving videos on disk), for example:

python -u habitat_baselines/evaluate_ppo.py   
...
--count-test-episodes 2 \
--video-option tensorboard \
--tensorboard-dir tb_eval \
--model-path data/checkpoints/ckpt.xx.pth

The above command should generate navigation episode recordings and display them on tensorboard like this: