List of our studies related to reinforcement learning
Dohyeong Kim and Songhwai Oh, "Efficient Off-Policy Safe Reinforcement Learning Using Trust Region Conditional Value at Risk," IEEE Robotics and Automation Letters, vol. 7, no. 3, pp. 7644-7651, Jul. 2022. [paper][code]
Dohyeong Kim and Songhwai Oh, “TRC: Trust region conditional value at risk for safe reinforcement learning,” IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 2621–2628, Apr. 2022. [paper][code]
Mineui Hong, Kyungjae Lee, Minjae Kang, Wonsuhk Jung, and Songhwai Oh,`Dynamics-Aware-Metric-Embedding: Metric Learning in a Latent Space for Visual Planning,' IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 3388-3395, Apr. 2022. [paper][code]
Jae In Kim, Mineui Hong, Kyungjae Lee, DongWook Kim, Yong-Lae Park, and Songhwai Oh, "Learning to Walk A Tripod Mobile Robot Using Nonlinear Soft Vibration Actuators with Entropy Adaptive Reinforcement Learning," IEEE International Conference on Robotics and Automation (ICRA), May 2020. (RA-L option) [paper][code]
Minjae Kang*, Kyungjae Lee*, and Songhwai Oh, "Soft Action Particle Deep Reinforcement Learning for a Continuous Action Space," in Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Nov. 2019. [paper] [code]
Kyungjae Lee, Sungyub Kim, Sungbin Lim, Sungjoon Choi, and Songhwai Oh, "Tsallis Reinforcement Learning: A Unified Framework for Maximum Entropy Reinforcement Learning," arXiv preprint arXiv:1902.00137, Feb. 2019. [paper] [code]
Kyungjae Lee. [code]