Deep Hierarchical Planning from Pixels

Official implementation of the Director algorithm in TensorFlow 2.

If you find this code useful, please reference in your paper:

@article{hafner2022director,
  title={Deep Hierarchical Planning from Pixels},
  author={Hafner, Danijar and Lee, Kuang-Huei and Fischer, Ian and Abbeel, Pieter},
  journal={Advances in Neural Information Processing Systems},
  year={2022}
}

How does Director work?

Director is a practical and robust algorithm for hierarchical reinforcement learning. To solve long horizon tasks end-to-end from sparse rewards, Director learns to break down tasks into internal subgoals. Its manager policy selects subgoals that trade off exploratory and extrinsic value, its worker policy learns to achieve the goals through low-level actions. Both policies are trained from imagined trajectories predicted by a learned world model. To support the manager in choosing realistic goals, a goal autoencoder compresses and quantizes previously encountered representations. The manager chooses its goals in this compact space. All components are trained concurrently.

For more information:

• Google AI Blog
• Project website
• Research paper

Running the Agent

Either use embodied/Dockerfile or follow the manual instructions below.

Install dependencies:

pip install -r requirements.txt

Train agent:

python embodied/agents/director/train.py \
  --logdir ~/logdir/$(date +%Y%m%d-%H%M%S) \
  --configs dmc_vision \
  --task dmc_walker_walk

See agents/director/configs.yaml for available flags and embodied/envs/__init__.py for available envs.

Using the Tasks

The HRL environments are in embodied/envs/pinpad.py and embodied/envs/loconav.py.

For video summary using FFmpeg,

export LD_LIBRARY_PATH=/content/conda-env/lib:/usr/local/nvidia/lib:/usr/local/nvidia/lib64