Launch basic training with command:
python cg_runner.py --policy <policy> --env <env>.
Progress logging and checkpoints can be found in the /save directory.
For other argument options, look into args.py, env_maker.py and policy_maker.py.
Currently available <policy>:
de(decentralized)dicg_ce(DICG-CE)proximal_cg(proximity-based coordination graph)
Currently available <env>:
meet(meeting in the grid world)predprey(predator-prey)traffic(hard mode traffic junction)