def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# Setup required for unit tests.
print("Unit testing CWD:", os.getcwd())
self.config = DotDict.from_json("../Configurations/ModelConfigs/MuzeroCartpole.json")
self.g = GymGame('CartPole-v1')
self.net = GymNet(self.g, self.config.net_args)
self.mcts = MuZeroMCTS(self.g, self.net, self.config.args)
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# Setup required for unit tests.
print("Unit testing CWD:", os.getcwd())
self.config = DotDict.from_json("../Configurations/ModelConfigs/MuzeroBoard.json")
self.g = HexGame(self.hex_board_size)
self.net = HexNet(self.g, self.config.net_args)
self.mcts = MuZeroMCTS(self.g, self.net, self.config.args)
def __init__(self,
game,
arg_file: typing.Optional[str] = None,
name: str = "") -> None:
super().__init__(game, arg_file, name, parametric=True)
if self.player_args is not None:
self.args = DotDict.from_json(self.player_args)
self.model = DefaultMuZero(self.game, self.args.net_args,
self.args.architecture)
self.search_engine = MuZeroMCTS(self.game, self.model,
self.args.args)
self.name = self.args.name
def __init__(self,
game,
arg_file: typing.Optional[str] = None,
name: str = "") -> None:
super().__init__(game, arg_file, name, parametric=True)
if self.player_args is not None:
# Initialize AlphaZero by loading its parameter config and constructing the network and search classes.
self.args = DotDict.from_json(self.player_args)
self.model = DefaultAlphaZero(self.game, self.args.net_args,
self.args.architecture)
self.search_engine = AlphaZeroMCTS(self.game, self.model,
self.args.args)
self.name = self.args.name
def __init__(self,
game,
nested_config: typing.Optional[DotDict] = None,
name: str = "") -> None:
super().__init__(game, nested_config.file, name, parametric=True)
if self.player_args is not None:
self.args = DotDict.from_json(self.player_args)
self.model = BlindMuZero(self.game, self.args.net_args,
self.args.architecture,
nested_config.refresh_freq)
self.model.bind(self.history.actions)
self.search_engine = MuZeroMCTS(self.game, self.model,
self.args.args)
self.name = self.args.name
def __init__(self,
game,
nested_config: typing.Optional[DotDict] = None,
name: str = "") -> None:
super().__init__(game, nested_config.file, name, parametric=True)
if self.player_args is not None:
# Initialize MuZero by loading its parameter config and constructing the network and search classes.
# Additionally assign/ bind internal MDP memory to enable planning strictly within the learned model.
self.args = DotDict.from_json(self.player_args)
self.model = BlindMuZero(self.game, self.args.net_args,
self.args.architecture,
nested_config.refresh_freq)
self.model.bind(self.history.actions)
self.search_engine = MuZeroMCTS(self.game, self.model,
self.args.args)
self.name = self.args.name
def __init__(self, experiment_file: str) -> None:
"""
Initialize the experiment data container using a string path to a .json settings file.
Not all variables are initialized directly (require an explicit call to construct) seeing as
this may bloat the memory with large implementations when performing a large number of experiments sequentially.
:param experiment_file: str Path to .json file containing experiment details.
"""
self.experiment_args = DotDict.from_json(experiment_file)
self.type = self.experiment_args.experiment
self.name = self.experiment_args.name
self.output_directory = self.output_directory = f'./out/{self.experiment_args.output_dir}/'
self.game_config = None
self.game = None
self.ablation_base = None
self.ablation_grid = None
self.player_configs = list()
def __enter__(self) -> AblationAnalysis:
"""
Initialize experiment by generating all ModelConfigs as specified by the hyperparameter grid,
and storing them in a temporary folder. All config files will be assigned an unique name, which will later
be accessed for training agents asynchronously.
"""
if not os.path.exists(self.config_dir):
os.makedirs(self.config_dir)
# First construct all possible hyperparameter configuration JSON contents.
self.configs = list()
base_config = DotDict.from_json(self.experiment.ablation_base.config)
for param in self.experiment.ablation_grid:
config = base_config.copy()
config.recursive_update(param)
self.configs.append(config)
# Save ablation analysis configuration using time annotation.
dt = datetime.now().strftime("%Y%m%d-%H%M%S")
schedule = DotDict({i: self.experiment.ablation_grid[i] for i in range(len(self.experiment.ablation_grid))})
schedule.to_json(os.path.join(self.experiment.output_directory, f'ablation_schedule_{dt}.json'))
# Store/ generate all unique JSON config files annotated by time and repetition number.
for run in range(self.experiment.experiment_args.num_repeat):
for i, config in enumerate(self.configs):
c = config.copy() # Note: shallow copy.
run_config_name = f'rep{run}_config{i}_dt{dt}'
c.name = f'{c.name}_{run_config_name}'
out = os.path.join(self.experiment.output_directory, c.args.checkpoint, run_config_name)
c.args.checkpoint = out
c.args.load_folder_file = (out, c.args.load_folder_file[1])
if not os.path.exists(c.args.checkpoint):
os.makedirs(c.args.checkpoint)
config_file = os.path.join(self.config_dir, run_config_name) + '.json'
c.to_json(config_file)
self.files.append(config_file)
return self
def __enter__(self) -> AblationAnalysis:
if not os.path.exists(self.config_dir):
os.makedirs(self.config_dir)
self.configs = list()
base_config = DotDict.from_json(self.experiment.ablation_base.config)
for param in self.experiment.ablation_grid:
config = base_config.copy()
config.recursive_update(param)
self.configs.append(config)
dt = datetime.now().strftime("%Y%m%d-%H%M%S")
schedule = DotDict({
i: self.experiment.ablation_grid[i]
for i in range(len(self.experiment.ablation_grid))
})
schedule.to_json(
os.path.join(self.experiment.output_directory,
f'ablation_schedule_{dt}.json'))
for run in range(self.experiment.experiment_args.num_repeat):
for i, config in enumerate(self.configs):
c = config.copy() # Note: shallow copy.
run_config_name = f'rep{run}_config{i}_dt{dt}'
c.name = f'{c.name}_{run_config_name}'
out = os.path.join(self.experiment.output_directory,
c.args.checkpoint, run_config_name)
c.args.checkpoint = out
c.args.load_folder_file = (out, c.args.load_folder_file[1])
if not os.path.exists(c.args.checkpoint):
os.makedirs(c.args.checkpoint)
config_file = os.path.join(self.config_dir,
run_config_name) + '.json'
c.to_json(config_file)
self.files.append(config_file)
return self