T. Kanazawa, C. Gupta. Latent-Conditioned Policy Gradient for Multi-Objective Deep Reinforcement Learning. Proceedings of the 32nd International Conference on Artificial Neural Networks (ICANN 2023), Lecture Notes in Computer Science. 2023. 14259. 63-76
J. Hu, H. Wang, H.-K. Tang, T. Kanazawa, C. Gupta, A. Farahat. Knowledge-enhanced reinforcement learning for multi-machine integrated production and maintenance scheduling. Computers & Industrial Engineering. 2023. 185. 109631. 1-13
T. Kanazawa, C. Gupta. Sample-based Uncertainty Quantification with a Single Deterministic Neural Network. Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022). 2022. 1. 292-304
T. Kanazawa, C. Gupta, H. Wang. Distributional Actor-Critic Ensemble for Uncertainty-Aware Continuous Control. Proceedings of the 2022 International Joint Conference on Neural Networks (IJCNN 2022). 2022. 1-10
T. Kanazawa. One-Parameter Family of New Acquisition Functions for Efficient Global Optimization. Proceedings for the 2022 International Joint Conference on Neural Networks (IJCNN 2022). 2022. 1-8
Latent-Conditioned Policy Gradient for Multi-Objective Deep Reinforcement Learning
(The 32nd International Conference on Artificial Neural Networks (ICANN 2023) 2023)
Sample-based Uncertainty Quantification with a Single Deterministic Neural Network
(The 14th International Conference on Neural Computation Theory and Applications (IJCCI/NCTA 2022) 2022)
Distributional Actor-Critic Ensemble for Uncertainty-Aware Continuous Control
(The 2022 International Joint Conference on Neural Networks (IJCNN 2022) 2022)
One-Parameter Family of New Acquisition Functions for Efficient Global Optimization
(The 2022 International Joint Conference on Neural Networks (IJCNN 2022) 2022)