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Source code for ding.rl_utils.beta_function

"""
Referenced papar <Implicit Quantile Networks for Distributional Reinforcement Learning>
"""
import torch
from typing import Union

beta_function_map = {}

beta_function_map['uniform'] = lambda x: x

# For beta functions, concavity corresponds to risk-averse and convexity to risk-seeking policies


# For CPW, eta = 0.71 most closely match human subjects
# this function is locally concave for small values of τ and becomes locally convex for larger values of τ
[docs]def cpw(x: Union[torch.Tensor, float], eta: float = 0.71) -> Union[torch.Tensor, float]: """ Overview: The implementation of CPW function. Arguments: - x (:obj:`Union[torch.Tensor, float]`): The input value. - eta (:obj:`float`): The hyperparameter of CPW function. Returns: - output (:obj:`Union[torch.Tensor, float]`): The output value. """ return (x ** eta) / ((x ** eta + (1 - x) ** eta) ** (1 / eta))
beta_function_map['CPW'] = cpw # CVaR is risk-averse
[docs]def CVaR(x: Union[torch.Tensor, float], eta: float = 0.71) -> Union[torch.Tensor, float]: """ Overview: The implementation of CVaR function, which is a risk-averse function. Arguments: - x (:obj:`Union[torch.Tensor, float]`): The input value. - eta (:obj:`float`): The hyperparameter of CVaR function. Returns: - output (:obj:`Union[torch.Tensor, float]`): The output value. """ assert eta <= 1.0 return x * eta
beta_function_map['CVaR'] = CVaR # risk-averse (eta < 0) or risk-seeking (eta > 0) def Pow(x: Union[torch.Tensor, float], eta: float = 0.0) -> Union[torch.Tensor, float]: """ Overview: The implementation of Pow function, which is risk-averse when eta < 0 and risk-seeking when eta > 0. Arguments: - x (:obj:`Union[torch.Tensor, float]`): The input value. - eta (:obj:`float`): The hyperparameter of Pow function. Returns: - output (:obj:`Union[torch.Tensor, float]`): The output value. """ if eta >= 0: return x ** (1 / (1 + eta)) else: return 1 - (1 - x) ** (1 / 1 - eta) beta_function_map['Pow'] = Pow