Source code for gaggle.arguments.problem_args

from dataclasses import dataclass, field


[docs]@dataclass class ProblemArgs: """ Argument class that contains the arguments relating to problems """ CONFIG_KEY = "problem_args" problem_name: str = field(default="cartpole", metadata={ "help": "problem to solve", }) batch_size: int = field(default=-1, metadata={ "help": "batch size for training. Only relevant for classification and other dataset-based problems." }) eval_batch_size: int = field(default=-1, metadata={ "help": "batch size for inference. Only relevant for classification and other dataset-based problems." }) dataset_root: str = field(default=None, metadata={ "help": "path to the data on the local storage. Only relevant for classification and other " "dataset-based problems." }) max_size_train: int = field(default=None, metadata={ "help": "maximum size of the training data (in number of samples). Samples a subset randomly." }) max_size_val: int = field(default=None, metadata={ "help": "maximum size of the evaluation data (in number of samples). Samples a subset randomly." }) seed: int = field(default=1337, metadata={ "help": "seed to fix randomness" }) steps: int = field(default=1, metadata={ "help": "number of steps to take in the environment for a single run. Only relevant for rl problems." }) runs: int = field(default=1, metadata={ "help": "number of runs per evaluation. Only relevant for rl problems." }) gui: bool = field(default=False, metadata={ "help": "if the environment has a gui, display it if True. Only relevant for rl problems with a gui (OpenAI Gym" "problems)" }) stop_on_done: bool = field(default=True, metadata={ "help": "whether to stop taking steps until self.steps amounts of steps have elapsed regardless of whether" "the environment is done with the run or not" })