README.md

baselines

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 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 "tasks/pointnav.yaml" \

test:

python -u 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 "tasks/pointnav.yaml" \

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

Classic

SLAM (coming soon)