Reinforcement Learning
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.
Contrastive Modules with Temporal Attention for Multi-Task Reinforcement Learning
We conducted the experiment on Meta-World, a multi-task RL benchmark containing various robotics manipulation tasks.
Guided Meta-Policy Search
Russell Mendonca, Abhishek Gupta, Rosen Kralev, Pieter Abbeel, Sergey Levine, Chelsea Finn
Meta-learning is a promising approach for using previous experience across a breadth of tasks to significantly accelerate learning ofnewtasks.