AI-Assisted Decision-Making and Risk Evaluation in Uncertain Environment Using Stochastic Inverse Reinforcement Learning: American Football as a Case Study

Abstract

In this work, we focus on the development of an AI technology to support decision making for people in leadership positions while facing uncertain environments. We demonstrate an efficient approach based on a stochastic inverse reinforcement leaning (IRL) algorithm constructed by hybridizing the conventional Max-entropy IRL and mixture density network (MDN) for the prediction of transition probability. We took the case study of American football, a sports game with stochastic environment, since the number of yards gainable on the next offence in real American football is usually uncertain during strategy planning and decision making. The expert data for IRL are built using the American football 2017 season data in National Football League (NFL). The American football simulation environment was built by training MDN using the annual NFL data to generate the state transition probability for IRL. Under the framework of Max-Entropy IRL, optimal strategy was successfully obtained through a learnt reward function by trial-and-error communication with the MDN environment. To precisely evaluate the validity of the learnt policy, we have conducted a risk-return analysis and revealed that the trained IRL agent showed higher return and lower risk than the expert data, indicating that it is possible for the proposed IRL algorithm to learn superior policy than the one derived directly from the expert teaching data. Decision-making in an uncertain environment is a general issue, ranging from business operation to management. Our work presented here will likely serve as a general framework for optimal business operation and risk management and contribute especially to the portfolio’s optimization in the financial and energy trading market.

Tactical Intelligent Decision Modelling in Sports Competitions Based on Reinforcement Learning Algorithms

“…By analyzing past game data and simulating various scenarios, these algorithms can suggest strategies that maximize the team's chances of scoring while minimizing defensive vulnerabilities. Furthermore, reinforcement learning can also be applied to individual athlete performance optimization [7]. By analyzing biomechanical data, training regimes, and performance metrics, algorithms can tailor personalized training programs to enhance an athlete's strengths and mitigate weaknesses.…”

“…Tactical intelligent decision modeling in sports competitions, powered by reinforcement learning algorithms, represents a sophisticated fusion of data science and sports analytics aimed at optimizing team strategies and individual performances [8]. This approach involves leveraging the wealth of data available in modern sportsfrom player statistics and game dynamics to opponent behavior and environmental factors-to derive actionable insights that can enhance decision-making at various levels of competition [9]. Reinforcement learning algorithms, a subset of machine learning, are particularly well-suited for this task.…”