4 papers
Deep Reinforcement Learning with Buffered Quantile Objectives
Mohammad Alipour-vaezi, Sajad Khodadadian
Quantile-based reinforcement learning provides an interpretable approach to risk-sensitive decision-making by optimizing a prescribed quantile of the cumulative-return distribution…
Risk-Sensitive Reinforcement Learning with Smoothed Quantile Objectives
Mohammad Alipour-Vaezi, Huaiyang Zhong, Sajad Khodadadian
Reinforcement Learning (RL) has achieved tremendous success in recent years. However, the classical foundations of RL do not account for the risk sensitivity of the objective funct…
Optimistic Reinforcement Learning with Quantile Objectives
Mohammad Alipour-Vaezi, Huaiyang Zhong, Kwok-Leung Tsui +1
Reinforcement Learning (RL) has achieved tremendous success in recent years. However, the classical foundations of RL do not account for the risk sensitivity of the objective funct…
Data-Driven Portfolio Management for Motion Pictures Industry: A New Data-Driven Optimization Methodology Using a Large Language Model as the Expert
Mohammad Alipour-Vaezi, Kwok-Leung Tsui
Portfolio management is one of the unresponded problems of the Motion Pictures Industry (MPI). To design an optimal portfolio for an MPI distributor, it is essential to predict the…