def main():
raylab.register_all_agents()
raylab.register_all_environments()
with suppress(KeyboardInterrupt):
parser = create_parser()
args = parser.parse_args()
run(args, parser)
def main():
env_args = {
"forest_data_path": "/Users/anmartin/Projects/summer_project/hl_planner/forest_data.tiff",
"simulation_data_path": "/Users/anmartin/Projects/FormationSimulation/fastsimulation.json",
"num_measurements": 6,
"max_forest_heights": [60, 90, 45, 38, 30, 76],
"orbit_altitude": 757000,
"draw_plot": True
}
parser = rollout.create_parser()
args = parser.parse_args()
register_env("offline-orekit", lambda _: OfflineOrekitEnv(env_args))
rollout.run(args, parser)
def train_agent(config):
parser = argparse.ArgumentParser(
description="Train or Run an RLlib Agent.",
formatter_class=argparse.RawDescriptionHelpFormatter)
subcommand_group = parser.add_subparsers(
help="Commands to train or run an RLlib agent.", dest="command")
train_parser = train.create_parser(
lambda **kwargs: subcommand_group.add_parser("train", **kwargs))
rollout_parser = rollout.create_parser(
lambda **kwargs: subcommand_group.add_parser("rollout", **kwargs))
options = parser.parse_args()
register_env('env_mp500lwa4dpg70', env_creator)
configFromYaml = {
'train-ppo': {
'env': 'env_mp500lwa4dpg70',
'run': 'PPO',
'config': config,
'checkpoint_freq': 1000,
#'local_dir': "~/train_results",
'stop': {
"timesteps_total": 1e10,
},
}
}
ray.init()
if options.command == "train":
configFromYaml['train-ppo']['config']['env_config'] = env_input_config(
True)
run_experiments(configFromYaml)
#train.run(options, train_parser)
elif options.command == "rollout":
options.run = configFromYaml['train-ppo']['run']
options.env = configFromYaml['train-ppo']['env']
options.no_render = True
options.steps = 1000
options.out = None
configFromYaml['train-ppo']['config']['env_config'] = env_input_config(
False)
configFromYaml['train-ppo']['config']['monitor'] = True
options.config = configFromYaml['train-ppo']['config']
rollout.run(options, rollout_parser)
else:
parser.print_help()
def cli():
parser = argparse.ArgumentParser(
description="Train or Run an RLlib Agent.",
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog=EXAMPLE_USAGE)
subcommand_group = parser.add_subparsers(
help="Commands to train or run an RLlib agent.", dest="command")
# see _SubParsersAction.add_parser in
# https://github.com/python/cpython/blob/master/Lib/argparse.py
train_parser = train.create_parser(
lambda **kwargs: subcommand_group.add_parser("train", **kwargs))
rollout_parser = rollout.create_parser(
lambda **kwargs: subcommand_group.add_parser("rollout", **kwargs))
options = parser.parse_args()
if options.command == "train":
train.run(options, train_parser)
elif options.command == "rollout":
rollout.run(options, rollout_parser)
else:
parser.print_help()
'--run',
'SAC',
'--env',
'rocketmeister',
'--episodes',
'10',
# '--no-render',
])
config = {
'env_config': {
# "export_frames": True,
"export_states": True,
'export_string': 'Training1', # filename prefix for exports
},
}
config_json = json.dumps(config)
parser = rollout.create_parser()
args = parser.parse_args(string.split() + ['--config', config_json])
# ──────────────────────────────────────────────────────────────────────────
# if you want to automate this, by calling rollout.run() multiple times, you
# uncomment the following lines too. They need to called before calling
# rollout.run() a second, third, etc. time
# ray.shutdown()
# tune.register_env("rocketgame", lambda c: MultiEnv(c))
# from ray.rllib import _register_all
# _register_all()
# ──────────────────────────────────────────────────────────────────────────
rollout.run(args, parser)
"""
This script wraps the rllib rollout command but uses the custom environment.
"""
from ray.rllib.rollout import create_parser, run
from ray.tune.registry import register_env
from .env import register_envs
register_envs()
if __name__ == '__main__':
parser = create_parser()
parser.set_defaults(env='CavalryVsInfantry', no_render=True)
args = parser.parse_args()
run(args, parser)
def create_parser_custom():
parser = create_parser()
parser.add_argument(
'--dbg',
action='store_true',
help='Full debug mode (also enables local-mode)',
)
parser.add_argument(
'--num-episodes',
default=1000,
type=int,
help='Number of full episodes to rollout',
)
parser.add_argument(
'--num-agents',
default=-1,
type=int,
help=
'Allows to set number of agents less than number of players, to allow humans to join the match'
'Default value (-1) means number of agents is the same as max number of players',
)
parser.add_argument(
'--num-bots',
default=-1,
type=int,
help=
'Add classic (non-neural) bots to the match. If default (-1) then use number of bots specified in env cfg',
)
parser.add_argument(
'--num-humans',
default=0,
type=int,
help='Meatbags want to play?',
)
parser.add_argument(
'--fps',
default=0,
type=int,
help='Enable sync mode with adjustable FPS.'
'Default (0) means default Doom FPS (~35). Leave at 0 for multiplayer',
)
parser.add_argument(
'--bot-difficulty',
default=150,
type=int,
help='Adjust bot difficulty',
)
parser.add_argument(
'--env-frameskip',
default=1,
type=int,
help=
'Usually frameskip is handled by the rollout loop for smooth rendering, but this is also an option',
)
parser.add_argument(
'--render-action-repeat',
default=-1,
type=int,
help=
'Repeat an action that many frames during rollout. -1 (default) means read from env config',
)
parser.add_argument(
'--record-to',
default=join(os.getcwd(), '..', 'doom_episodes'),
type=str,
help='Record episodes to this folder using Doom functionality',
)
parser.add_argument(
'--custom-res',
default=None,
type=str,
help='Custom resolution string (e.g. 1920x1080).'
'Can affect performance of the model if does not match the resolution the model was trained on!',
)
return parser
