137 citations · 527 across the 41 of their papers we have counts for
6 papers · 2 filters
Apprenticeship Learning via Frank-Wolfe
Tom Zahavy, Alon Cohen, Haim Kaplan +1
We consider the applications of the Frank-Wolfe (FW) algorithm for Apprenticeship Learning (AL). In this setting, we are given a Markov Decision Process (MDP) without an explicit r…
Inverse Reinforcement Learning in Contextual MDPs
Stav Belogolovsky, Philip Korsunsky, Shie Mannor +2
We consider the task of Inverse Reinforcement Learning in Contextual Markov Decision Processes (MDPs). In this setting, contexts, which define the reward and transition kernel, are…
Unknown mixing times in apprenticeship and reinforcement learning
Tom Zahavy, Alon Cohen, Haim Kaplan +1
We derive and analyze learning algorithms for apprenticeship learning, policy evaluation, and policy gradient for average reward criteria. Existing algorithms explicitly require an…
Action Assembly: Sparse Imitation Learning for Text Based Games with Combinatorial Action Spaces
Chen Tessler, Tom Zahavy, Deborah Cohen +2
We propose a computationally efficient algorithm that combines compressed sensing with imitation learning to solve text-based games with combinatorial action spaces. Specifically,…
Planning in Hierarchical Reinforcement Learning: Guarantees for Using Local Policies
Tom Zahavy, Avinatan Hasidim, Haim Kaplan +1
We consider a settings of hierarchical reinforcement learning, in which the reward is a sum of components. For each component we are given a policy that maximizes it and our goal i…
Deep Neural Linear Bandits: Overcoming Catastrophic Forgetting through Likelihood Matching
Tom Zahavy, Shie Mannor
We study the neural-linear bandit model for solving sequential decision-making problems with high dimensional side information. Neural-linear bandits leverage the representation po…