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Hieros

Implemenation of the HIERarchical imagionation On Structured State Space Sequence Models (HIEROS) paper in pytorch. This repository is based on the DreamerV3, DreamerV3 in pytorch and S5 in pytorch repositories.

Installation

Install pip dependencies:

pip install -r requirements.txt

Install required tools:

sudo apt update && sudo apt install -y wget unrar

Install atari roms:

bash embodied/scripts/install-atari.sh

Usage

To train a model on a atari game, run:

python hieros/train.py --configs atari100k --task=atari_alien

You can specify the task to train on with the --task flag. The available tasks are:

atari_alien, atari_amidar, atari_assault, atari_asterix, atari_bank_heist, atari_battle_zone, atari_boxing, atari_breakout, atari_chopper_command, atari_crazy_climber, atari_demon_attack, atari_freeway, atari_frostbite, atari_gopher, atari_hero, atari_jamesbond, atari_kangaroo, atari_krull, atari_kung_fu_master, atari_ms_pacman, atari_pong, atari_private_eye, atari_qbert, atari_road_runner, atari_seaquest

We also support a wide range of other benchmarks. For this, please reference the hieros/config.yml to find different configurations. For example, to train on the dmc_vision task, run:

python hieros/train.py --configs dmc_vision --task=dmc_cheetah_run

All flags available in hieros/config.yml are configurable as command line arguments. For example, to train on the atari_alien task with a different number of layers, run:

python hieros/train.py --configs atari100k --task=atari_alien --max_hierarchy=2

We also include an implementation of the original DreamerV3 model, which is accessible with --model_name=dreamer.

The metrics are logged to tensorboard by default. To visualize the training progress, run:

tensorboard --logdir=logs

With these training statistics, you can also reproduce the plots in the paper.

Repository

The repository is structured as follows:

• hieros/ contains the implementation and training code of the HIEROS model.
• embodied/ contains the implementation of some basic tools like logging, replay buffers, environments, etc. This is largely copied from here
• resettable_s5/ contains our implementation of the resettable S5 model used for the S5WM. This is based on the pytorch s5 implementation
• experiments/ contains wandb sweep configurations for the experiments in the paper.
• sampler_visualization.py contains code to visualize the sampling methods used in the paper (ETBS and the standard uniform sampling).