254 citations · 362 across the 10 of their papers we have counts for
4 papers · 1 filter
Learning Robust Options
Daniel J. Mankowitz, Timothy A. Mann, Pierre-Luc Bacon +2
Robust reinforcement learning aims to produce policies that have strong guarantees even in the face of environments/transition models whose parameters have strong uncertainty. Exis…
Situationally Aware Options
Daniel J. Mankowitz, Aviv Tamar, Shie Mannor
Hierarchical abstractions, also known as options -- a type of temporally extended action (Sutton et. al. 1999) that enables a reinforcement learning agent to plan at a higher level…
Shallow Updates for Deep Reinforcement Learning
Nir Levine, Tom Zahavy, Daniel J. Mankowitz +2
Deep reinforcement learning (DRL) methods such as the Deep Q-Network (DQN) have achieved state-of-the-art results in a variety of challenging, high-dimensional domains. This succes…
Bootstrapping Skills
Daniel J. Mankowitz, Timothy A. Mann, Shie Mannor
The monolithic approach to policy representation in Markov Decision Processes (MDPs) looks for a single policy that can be represented as a function from states to actions. For the…