Reinforcement Learning
Convergent Policy Optimization for Safe Reinforcement Learning
Ming Yu, Zhuoran Yang, Mladen Kolar, Zhaoran Wang
Given ,J ( )andD ( )arethesample (i.e., atrajectory) . Note J ( ) and D ( ) are randomness J ( )andD ( )todenote anda ClearlyweJ( )= E J ( ) andD( )= E D ( ) .
Unsupervised Learning of Object Keypoints for Perception and Control
Tejas D. Kulkarni, Ankush Gupta, Catalin Ionescu, Sebastian Borgeaud, Malcolm Reynolds, Andrew Zisserman, Volodymyr Mnih
Neural Information Processing Systems http://nips.cc/
5c186016d0844767209dc36e9e61441b-Paper-Conference.pdf
DeMa's focus on sequences diminishes approximately exponentially.
Doubly Mild Generalization for Offline Reinforcement Learning Yixiu Mao 1, Qi Wang 1, Y un Qu
Offline Reinforcement Learning (RL) suffers from the extrapolation error and value overestimation. From a generalization perspective, this issue can be attributed to the over-generalization of value functions or policies towards out-of-distribution (OOD) actions.
Doubly-Robust Lasso Bandit
While therewardcompensation mechanism isunknown,the learner can adapt his (her) decision to the past reward feedback so as to maximize the sum of rewards.
Unsupervised Curricula for Visual Meta-Reinforcement Learning
Allan Jabri, Kyle Hsu, Abhishek Gupta, Ben Eysenbach, Sergey Levine, Chelsea Finn
Neural Information Processing Systems http://nips.cc/
Scalable Primal-Dual Actor-Critic Method for Safe Multi-Agent RL with General Utilities
In fact, the interaction of these two aspects requires addressing the fact that each agent's own safety constraint requires information from all others.
Scalable Primal-Dual Actor-Critic Method for Safe Multi-Agent RL with General Utilities
In fact, the interaction of these two aspects requires addressing the fact that each agent's own safety constraint requires information from all others.
