In this project, we designed a reinforcement learning environment to solve the single round-robin tournament scheduling problem. The article can be found at

J. C. Peng, A. D. Clark and A. Dahbura, "Introducing Human Corrective Multi-Team SRR Sports Scheduling via Reinforcement Learning," 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 2021, pp. 1-7, doi: 10.1109/SSCI50451.2021.9660171.

My duties in this project include:

• Co-designed Markov Decision Process modelling for single round-robin tournament scheduling problems

• Designed innovative backtracking actions in action space, significantly increasing robustness

• Developed custom reinforcement learning environment for round-robin tournament scheduling problems by OpenAI Gym

• Trained reinforcement learning agents by ACKTR algorithm and successfully generated sample schedules with least number of breaks in single round-robin cases

In this project, we designed a new novel supervised learning model named Comonotone-Independence Bayesian Classifier (CIBer). The manuscript is attached.

My duties in this project include:

• Co-designed and developed novel supervised learning model named Comonotone-Independence Bayesian Classifier (CIBer), whose core techniques include:

  • Utilizing hierarchical clustering algorithm to search for optimal partition of all predictive features

  • Using comonotonicity to model dependence structure and to estimate conditional joint probability

• Co-designed and implemented Joint Encoding algorithm used to encode multiple categorical variables by numerical values while preserving their internal relationship

• Compared CIBer with classical supervised learning models including Logistic regression, SVM, Decision tree, MLP, KNN, and Naïve Bayes on several insurance-related datasets, obtaining out-of-sample accuracy over 10% higher than any other model and AUC values over 0.08 higher than any other model on every dataset

• Extended model with singleton classifier to more robust version with ensemble learning

This is the my master thesis project in Johns Hopkins University. In this project, I designed an approximation algorithm to search for the optimal strategy in the general Mozart Cafe problem. The manuscript is attached.

My duties in this project include:

• Designed novel k-Markovian parameterization technique as generic representation for any symmetric rendezvous search strategy in Mozart-Café problem

• Developed Monte Carlo simulation programs by Python to estimate expected rendezvous time for any symmetric rendezvous search strategy parameterized by our k-Markovian technique

• Utilized Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm on parameter optimization in k-Markovian modeling and reproduced optimal strategies in small size cases