def setup_class(cls):
os.makedirs("local_tests", exist_ok = True)
set_seeds()
repo_path = os.path.join( os.path.dirname(os.path.dirname(os.path.abspath(__file__))) )
if 'PYTHONPATH' not in os.environ: os.environ['PYTHONPATH'] = ''
if repo_path not in os.environ['PYTHONPATH']: os.environ['PYTHONPATH'] += f':{repo_path}'
'choices': [True, False],
},
}
if __name__ == "__main__":
description = r"""
___________________________________________________________________
/_/_/_/\ ______ _ ______ _ _______ _____ _ __ _____
/_/_/_/\/\ | ___ \ | | ___ \ | | | ___ \_ _| | / // ___|
/_/_/_/\/\/\| |_/ / | | |_/ / | | | |_/ / | | | |/ / \ `--.
\_\_\_\/\/\/| /| | | /| | | | ___ \ | | | \ `--. \
\_\_\_\/\/ | |\ \| |____ | |\ \| |_| | |_/ /_| |_| |\ \/\__/ /
\_\_\_\/ \_| \_\_____/ \_| \_|\___/\____/ \___/\_| \_/\____/
__________________________________________________________________
Start one or more Reinforcement Learning training session(s)
on the Rubik's Cube using config or CLI arguments.
"""
set_seeds()
parser = Parser(options,
description=description,
name='train',
description_last=True)
parsley = parser.parse()
TrainJob.clean_dir(parser.save_location)
jobs = [TrainJob(**settings) for settings in parsley]
for job in jobs:
job.execute()
def agent_optimize():
"""
Main way to run optimization. Hard coded to run optimization at 1 sec per game, but other behaviour can be set with CLI arguments seen by
running `python librubiks/solving/hyper_optim.py --help`.
Does not support config arguments.
NB: The path here is different to the one in runeval and runtrain:
It needs to be to folder containing model.pt! It doesen't work with parent folder.
Can work with runeval through
```
python librubiks/solving/hyper_optim.py --location example/net1/
python runeval.py --location example/ --optimized_params True
```
"""
set_seeds()
#Lot of overhead just for default argument niceness: latest model is latest
from runeval import train_folders
model_path = ''
if train_folders:
for folder in [train_folders[-1]] + glob(f"{train_folders[-1]}/*/"):
if os.path.isfile(os.path.join(folder, 'model.pt')):
model_path = os.path.join(folder)
break
parser = argparse.ArgumentParser(description='Optimize Monte Carlo Tree Search for one model')
parser.add_argument('--location', help='Folder which includes model.pt. Results will also be saved here',
type=str, default=model_path)
parser.add_argument('--iterations', help='Number of iterations of Bayesian Optimization',
type=int, default=125)
parser.add_argument('--agent', help='Name of agent corresponding to agent class in librubiks.solving.agents',
type=str, default='AStar', choices = ['AStar', 'MCTS', 'EGVM'])
parser.add_argument('--depth', help='Single number corresponding to the depth at which to test. If 0: run this at deep',
type=int, default=0)
parser.add_argument('--eval_games', help='Number of games to evaluate at depth',
type = int, default='100')
parser.add_argument('--save_optimal', help='If Tue, saves a JSON of optimal hyperparameters usable for runeval',
type=literal_eval, default=True, choices = [True, False])
parser.add_argument('--use_best', help="Set to True to use model-best.pt instead of model.pt.", type=literal_eval, default=True,
choices = [True, False])
parser.add_argument('--optim_lengths', help="Set to true to optimize against sol percentage / solution length. Else, simply use sol %", type=literal_eval,
default=True, choices = [True, False])
parser.add_argument('--optimizer', help="Either BO or grid", type=str, default="grid", choices = ("grid", "BO"))
args = parser.parse_args()
agent_name = args.agent
if agent_name == 'MCTS':
params = {
'c': (0.1, 100),
}
def prepper(params): return params
persistent_params = {
'net': Model.load(args.location, load_best=args.use_best),
'search_graph': True,
}
elif agent_name == 'AStar':
params = {
'lambda_': (0, 0.4),
'expansions': (1, 1000),
}
def prepper(params):
params['expansions'] = int(params['expansions'])
return params
persistent_params = {
'net': Model.load(args.location, load_best=args.use_best),
}
elif agent_name == 'EGVM':
params = {
'epsilon': (0, 0.5),
'workers': (1, 500),
'depth': (1, 250),
}
def prepper(params):
params['workers'] = int(params['workers'])
params['depth'] = int(params['depth'])
return params
persistent_params = {
'net': Model.load(args.location, load_best=args.use_best),
}
else:
raise NameError(f"{agent_name} does not correspond to a known agent, please pick either AStar, MCTS or EGVM")
logger = Logger(os.path.join(args.location, f'{agent_name}_optimization.log'), 'Optimization')
logger.log(f"{agent_name} optimization. Using network from {model_path}.")
logger.log(f"Received arguments: {vars(args)}")
agent = getattr(agents, agent_name)
evaluator = Evaluator(n_games=args.eval_games, max_time=5, scrambling_depths=range(0) if args.depth == 0 else [args.depth])
assert args.optimizer in ["BO", "grid"], f"Optimizer should be 'BO' or 'grid', not '{args.optimizer}'"
if args.optimizer == "BO":
optimizer = BayesianOptimizer(target_function=None, parameters=params, logger=logger)
else:
optimizer = GridSearch(target_function=None, parameters=params, logger=logger)
optimizer.objective_from_evaluator(evaluator, agent, persistent_params, param_prepper=prepper, optim_lengths=args.optim_lengths)
optimizer.optimize(args.iterations)
if args.save_optimal:
with open(os.path.join(args.location, f'{agent_name}_params.json'), 'w') as outfile:
json.dump(prepper(copy(optimizer.optimal)), outfile)