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A number of agents (PPO, MuZero) with a Perceiver-based NN architecture that can be trained to achieve goals in nethack/minihack environments.

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omega

This repo contains and implementation of an agent that can learn to maximise reward in environments with NetHack interface such as nle or MiniHack.

Crossing a river Fighting monsters in a narrow corridor

Repo highlights

How to train an agent

  1. Clone the repository:
git clone https://github.com/hr0nix/omega.git
  1. Run the docker container:
bash ./omega/docker/run_container.sh
  1. Create a new experiment based on one of the provided configs:
python3.8 ./tools/experiment_manager.py make --config ./configs/muzero/random_room_5x5.yaml --output-dir ./experiments/muzero_random_room_5x5
  1. Run the newly created experiment. You can optionally track the experiment using wandb (you will be asked if you want to, definitely recommended).
python3.8 ./tools/experiment_manager.py run --dir ./experiments/muzero_random_room_5x5 --gpu 0
  1. After some episodes are completed, you can visualize them:
python3.8 ./tools/experiment_manager.py play --file ./experiments/muzero_random_room_5x5/episodes/<EPISODE_FILENAME_HERE>

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A number of agents (PPO, MuZero) with a Perceiver-based NN architecture that can be trained to achieve goals in nethack/minihack environments.

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