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 main(algorithm, config):
parser = argparse.ArgumentParser()
parser.add_argument('--mode', type=str, default='train')
parser.add_argument('--checkpoint',
type=str,
default='/home/pyang/ray_results/paint')
args = parser.parse_args()
ModelCatalog.register_custom_model('paint_model', PaintModel)
ModelCatalog.register_custom_model('paint_layer_model', PaintLayerModel)
def env_creator(env_config):
return PaintGymEnv(**env_config)
tune.registry.register_env('robot_gym_env', env_creator)
experiment_config = {
'paint': {
'run': algorithm,
'env': 'robot_gym_env',
'stop': {
'training_iteration': 100000,
# 'timesteps_total': 2000000,
},
'config': config,
'checkpoint_freq': 200,
}
}
experiment_config['paint']['config']['callbacks'] = call_backs
if args.mode == 'train':
ray.init(object_store_memory=10000000000,
redis_max_memory=5000000000,
log_to_driver=True)
# ray.init(redis_address="141.3.81.143:6379")
experiment_config['paint']['config']['env_config'] = _make_env_config()
tune.run_experiments(experiment_config)
else:
experiment_config['paint']['config']['num_workers'] = 2
args.run = experiment_config['paint']['run']
args.env = experiment_config['paint']['env']
args.steps = 300
experiment_config['paint']['config']['env_config'] = _make_env_config(
is_train=False)
args.config = experiment_config['paint']['config']
args.out = None
args.no_render = True
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 main(algorithm, config):
parser = argparse.ArgumentParser()
parser.add_argument('--mode', type=str, default='train')
parser.add_argument('--checkpoint',
type=str,
default='/home/pyang/ray_results/paint')
args = parser.parse_args()
def env_creator(env_config):
return ParamTestEnv(**env_config)
tune.registry.register_env('param_test_env', env_creator)
experiment_config = {
'param_test': {
'run': algorithm,
'env': 'param_test_env',
'stop': {
'training_iteration': 10000,
},
'config': config,
'checkpoint_freq': 200,
}
}
if args.mode == 'train':
ray.init(object_store_memory=10000000000,
redis_max_memory=5000000000,
log_to_driver=True)
# ray.init(redis_address='141.3.81.145:6359')
tune.run_experiments(experiment_config)
else:
experiment_config['param_test']['config']['num_workers'] = 2
experiment_config['param_test']['config']['env_config'][
'train_mode'] = False
args.run = experiment_config['param_test']['run']
args.env = experiment_config['param_test']['env']
args.steps = 400
args.config = experiment_config['param_test']['config']
args.out = None
args.no_render = True
run(args, parser)
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()
'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)
parser = rollout.create_parser()
SSBMEnv.update_parser(parser)
args = parser.parse_args()
config = {
"env": ssbm_env.MultiSSBMEnv,
"env_config": {
"ssbm_config": args.__dict__.copy(),
"flat_obs": True,
"conv": "slippi",
"action_mode": "slippi_repeat",
},
"horizon": 1200,
"soft_horizon": True,
"num_workers": 0,
"autoregressive": True,
"residual": True,
"imitation": False,
"model": {
"custom_model": "human_action",
"use_lstm": True,
"lstm_cell_size": 256,
"lstm_use_prev_action_reward": True,
},
}
args.config = config
rollout.run(args, parser, config)