Summary of Safe Reinforcement Learning with Learned Non-markovian Safety Constraints, by Siow Meng Low and Akshat Kumar
Safe Reinforcement Learning with Learned Non-Markovian Safety Constraintsby Siow Meng Low, Akshat KumarFirst submitted to…
Safe Reinforcement Learning with Learned Non-Markovian Safety Constraintsby Siow Meng Low, Akshat KumarFirst submitted to…
RICE: Breaking Through the Training Bottlenecks of Reinforcement Learning with Explanationby Zelei Cheng, Xian Wu,…
Finite-Time Convergence and Sample Complexity of Actor-Critic Multi-Objective Reinforcement Learningby Tianchen Zhou, FNU Hairi, Haibo…
Linear Convergence of Independent Natural Policy Gradient in Games with Entropy Regularizationby Youbang Sun, Tao…
Taming Equilibrium Bias in Risk-Sensitive Multi-Agent Reinforcement Learningby Yingjie Fei, Ruitu XuFirst submitted to arxiv…
Quality-Weighted Vendi Scores And Their Application To Diverse Experimental Designby Quan Nguyen, Adji Bousso DiengFirst…
Proximal Curriculum with Task Correlations for Deep Reinforcement Learningby Georgios Tzannetos, Parameswaran Kamalaruban, Adish SinglaFirst…
Off-OAB: Off-Policy Policy Gradient Method with Optimal Action-Dependent Baselineby Wenjia Meng, Qian Zheng, Long Yang,…
CTD4 – A Deep Continuous Distributional Actor-Critic Agent with a Kalman Fusion of Multiple Criticsby…
UDUC: An Uncertainty-driven Approach for Learning-based Robust Controlby Yuan Zhang, Jasper Hoffmann, Joschka BoedeckerFirst submitted…