84 citations · 603 across the 59 of their papers we have counts for
7 papers · 1 filter
Corruption-robust exploration in episodic reinforcement learning
Thodoris Lykouris, Max Simchowitz, Aleksandrs Slivkins +1
We initiate the study of multi-stage episodic reinforcement learning under adversarial corruptions in both the rewards and the transition probabilities of the underlying system ext…
Policy Poisoning in Batch Reinforcement Learning and Control
Yuzhe Ma, Xuezhou Zhang, Wen Sun +1
We study a security threat to batch reinforcement learning and control where the attacker aims to poison the learned policy. The victim is a reinforcement learner / controller whic…
Optimal Sketching for Kronecker Product Regression and Low Rank Approximation
Huaian Diao, Rajesh Jayaram, Zhao Song +2
We study the Kronecker product regression problem, in which the design matrix is a Kronecker product of two or more matrices. Given for $i=1,2…
Provably Efficient Imitation Learning from Observation Alone
Wen Sun, Anirudh Vemula, Byron Boots +1
We study Imitation Learning (IL) from Observations alone (ILFO) in large-scale MDPs. While most IL algorithms rely on an expert to directly provide actions to the learner, in this…
Imitation Learning as -Divergence Minimization
Liyiming Ke, Sanjiban Choudhury, Matt Barnes +3
We address the problem of imitation learning with multi-modal demonstrations. Instead of attempting to learn all modes, we argue that in many tasks it is sufficient to imitate any…
Efficient Model-free Reinforcement Learning in Metric Spaces
Zhao Song, Wen Sun
Model-free Reinforcement Learning (RL) algorithms such as Q-learning [Watkins, Dayan 92] have been widely used in practice and can achieve human level performance in applications s…
Contrasting Exploration in Parameter and Action Space: A Zeroth-Order Optimization Perspective
Anirudh Vemula, Wen Sun, J. Andrew Bagnell
Black-box optimizers that explore in parameter space have often been shown to outperform more sophisticated action space exploration methods developed specifically for the reinforc…