Deep Reinforcement Learning (DRL) is a paradigm of artificial intelligence where an agent uses a neural network to learn which actions to take in a given environment. DRL has recently gained traction from being able to solve complex environments like driving simulators, 3D robotic control, and multiplayer-online-battle-arena video games. Numerous implementations of the state-of-the-art algorithms responsible for training these agents, like the Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) algorithms, currently exist. However, studies make the mistake of assuming implementations of the same algorithm to be consistent and thus, interchangeable. In this paper, through a differential testing lens, we present the results of studying the extent of implementation inconsistencies, their effect on the implementations' performance, as well as their impact on the conclusions of prior studies under the assumption of interchangeable implementations. The outcomes of our differential tests showed significant discrepancies between the tested algorithm implementations, indicating that they are not interchangeable. In particular, out of the five PPO implementations tested on 56 games, three implementations achieved superhuman performance for 50% of their total trials while the other two implementations only achieved superhuman performance for less than 15% of their total trials. Furthermore, the performance among the high-performing PPO implementations was found to differ significantly in nine games. As part of a meticulous manual analysis of the implementations' source code, we analyzed implementation discrepancies and determined that code-level inconsistencies primarily caused these discrepancies. Lastly, we replicated a study and showed that this assumption of implementation interchangeability was sufficient to flip experiment outcomes. Therefore, this calls for a shift in how implementations are being used. In addition, we recommend for (1) replicability studies for studies mistakenly assuming implementation interchangeability, (2) DRL researchers and practitioners to adopt the differential testing methodology proposed in this paper to combat implementation inconsistencies, and (3) the use of large environment suites.

Sports analytics encompasses the use of data science, AI, psychology, and IoT devices to improve sports performance, strategy, and decision-making. It involves collecting, processing, and interpreting data from various sources such as video recordings and scouting reports. The data is used to evaluate player and team performance, prevent injuries, and help coaches make informed decisions in game and training. We adopt Probabilistic Model Checking (PMC), a method commonly used in reliability analysis for complex safety systems, and explain how this method can be applied to sports strategy analytics to increase the chance of winning by taking into account the reliability of a player’s specific sub-skill sets. This paper describes how we have integrated PMC, machine learning, and computer vision to develop a new and complex system for sports strategy analytics. Finally, we discuss the vision of a new series of international sports analytics conferences (https://formal-analysis.com/isace/2023/).