In order to handle uncertainty in the human's discount factor γ within the CPL framework, one approach could be to introduce a probabilistic model for γ. This would involve treating γ as a random variable with a certain distribution, rather than a fixed value. By incorporating this uncertainty into the model, CPL could adapt to different possible values of γ and learn policies that are robust across a range of discount factors.
One way to implement this is through Bayesian methods, where the discount factor γ is treated as a latent variable with a prior distribution. During training, the model would learn the posterior distribution of γ given the data, allowing for uncertainty quantification. This Bayesian approach would enable CPL to make decisions that are more robust to variations in the discount factor, as it would consider the entire distribution of possible values rather than a single fixed value.
Another approach could involve incorporating γ as a hyperparameter in the model architecture. By treating γ as a tunable parameter that is optimized during training, CPL could adapt to different discount factors based on the data it receives. This would allow the model to dynamically adjust its behavior based on the observed preferences and the uncertainty in γ.
Overall, by introducing probabilistic modeling or treating γ as a hyperparameter, CPL could be extended to handle uncertainty in the human's discount factor γ, making the learned policies more adaptable and robust in real-world scenarios.