NeurIPS 2020: MineRL Competition Prioritized Dueling Double DQN (PDDDQN) Baseline with PFRL

This repository is a Prioritized Dueling Double DQN baseline submission example with PFRL, originated from the main MineRL Competition submission template and starter kit.

For detailed & latest documentation about the competition/template, see the original template repository.

This repository is a sample of the "Round 1" submission, i.e., the agents are trained locally.

test.py is the entrypoint script for Round 1.

The train.py, which is the entrypoint for Round 2, have not been checked if it could work on the MineRL Competition's submission system yet. To train this baseline agent, see "How to Train Baseline Agent on your own" section below.

train/ directory contains baseline agent's model weight files trained on MineRLObtainDiamondDenseVectorObf-v0.

List of current baselines

• Rainbow
• SQIL
• DQfD
• PDDDQN <-- We are here

How to Submit

After signing up the competition, specify your account data in aicrowd.json. See the official doc for detailed information.

Then you can create a submission by making a tag push to your repository on https://gitlab.aicrowd.com/. Any tag push (where the tag name begins with "submission-") to your repository is considered as a submission.

If everything works out correctly, you should be able to see your score on the competition leaderboard.

About Baseline Algorithm

This baseline consists of two main steps:

1. Apply K-means clustering for the action space with the demonstration dataset.
2. Apply PDDDQN algorithm on the discretized action space.

Each of steps utilizes existing libraries.
K-means in the step 1 is from scikit-learn, and PDDDQN in the step 2 is from PFRL, which is a Pytorch-based RL library.

How to Train Baseline Agent on your own

mod/ directory contains all you need to train agent locally:

pip install numpy scipy scikit-learn pandas tqdm joblib pfrl

# Don't forget to set this!
export MINERL_DATA_ROOT=<directory you want to store demonstration dataset>

python3 mod/dqn_family.py \
  --gpu 0 --env "MineRLObtainDiamondDenseVectorObf-v0"  \
  --outdir result \
  --noisy-net-sigma 0.5 --arch dueling --replay-capacity 300000 --replay-start-size 5000 --target-update-interval 10000 \
  --num-step-return 10 --agent DoubleDQN --monitor --lr 0.0000625 --adam-eps 0.00015 --prioritized --frame-stack 4 --frame-skip 4 \
  --gamma 0.99 --batch-accumulator mean

Team

The quick-start kit was authored by Shivam Khandelwal with help from William H. Guss

The competition is organized by the following team:

• William H. Guss (Carnegie Mellon University)
• Mario Ynocente Castro (Preferred Networks)
• Cayden Codel (Carnegie Mellon University)
• Katja Hofmann (Microsoft Research)
• Brandon Houghton (Carnegie Mellon University)
• Noboru Kuno (Microsoft Research)
• Crissman Loomis (Preferred Networks)
• Keisuke Nakata (Preferred Networks)
• Stephanie Milani (University of Maryland, Baltimore County and Carnegie Mellon University)
• Sharada Mohanty (AIcrowd)
• Diego Perez Liebana (Queen Mary University of London)
• Ruslan Salakhutdinov (Carnegie Mellon University)
• Shinya Shiroshita (Preferred Networks)
• Nicholay Topin (Carnegie Mellon University)
• Avinash Ummadisingu (Preferred Networks)
• Manuela Veloso (Carnegie Mellon University)
• Phillip Wang (Carnegie Mellon University)