hishamcse/doom_deathmatch_bots

A(n) APPO model trained on the doom_deathmatch_bots environment.

This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/

Codes

Github repos(Give a star if found useful):

• https://github.com/hishamcse/Advanced-DRL-Renegades-Game-Bots
• https://github.com/hishamcse/DRL-Renegades-Game-Bots
• https://github.com/hishamcse/Robo-Chess

Kaggle Notebook:

• https://www.kaggle.com/code/syedjarullahhisham/drl-huggingface-unit-8-ii-smpfc-vizdoom-deathmatch

Downloading the model

After installing Sample-Factory, download the model with:

python -m sample_factory.huggingface.load_from_hub -r hishamcse/doom_deathmatch_bots

Using the model

To run the model after download, use the enjoy script corresponding to this environment:

python -m .opt.conda.lib.python3.10.site-packages.ipykernel_launcher --algo=APPO --env=doom_deathmatch_bots --train_dir=./train_dir --experiment=doom_deathmatch_bots

You can also upload models to the Hugging Face Hub using the same script with the --push_to_hub flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details

Training with this model

To continue training with this model, use the train script corresponding to this environment:

python -m .opt.conda.lib.python3.10.site-packages.ipykernel_launcher --algo=APPO --env=doom_deathmatch_bots --train_dir=./train_dir --experiment=doom_deathmatch_bots --restart_behavior=resume --train_for_env_steps=10000000000

Note, you may have to adjust --train_for_env_steps to a suitably high number as the experiment will resume at the number of steps it concluded at.