catherinelee274 / neuralmmo2023

Neural MMO Competition 2023

Test that training runs

python train.py --local-mode true

Run training. This is very memory intensive!

You can change --num-envs and --rollout-batch-size to adjust memory usage

Also check out --device and --seed

The checkpoints are saved under --runs_dir with --run_name

python train.py --run-name <YOUR_RUN_NAME> --device <YOUR_DEVICE> --seed <YOUR_NUMBER> --num-envs 1 --rollout-batch-size 2**14

Evaluate checkpoints. After training, copy your checkpoints into policies

The below command will compare your checkpoints against the baseline policy

python evaluate.py -p policies

To generate a replay, create a directory with your checkpoints then run

python evaluate.py -p <YOUR_DIR> -r

NMMO Baseline Repository:

├── reinforcement_learning

│ ├── config.py

│ └── policy.py --> Your policy goes here

├── requirements.txt

└── train.py --> Train your policy here

Future

• inference with apache beam
• https://engineering.linkedin.com/teams/data/data-infrastructure/machine-learning-infrastructure
• https://www.linkedin.com/pulse/machine-learning-platforms-ai-lifecycle-management-mark-mondoka-holtc/
• FastTransformers
• Deepspeed
• https://www.tensorflow.org/tfx
• Ray, KubeFlow or W&B