Daniel J. Mankowitz

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Research Scientist at DeepMind. Doctor of Philosophy (Ph.D.) Reinforcement Learning at Technion Israel Institute of Technology.

An empirical investigation of the challenges of real-world reinforcement learning

Reinforcement learning (RL) has proven its worth in a series of artifici...
03/24/2020 ∙ by Gabriel Dulac-Arnold, et al. ∙ 9 ∙ share

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Robust Reinforcement Learning for Continuous Control with Model Misspecification

We provide a framework for incorporating robustness -- to perturbations ...
06/18/2019 ∙ by Daniel J. Mankowitz, et al. ∙ 0 ∙ share

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Action Assembly: Sparse Imitation Learning for Text Based Games with Combinatorial Action Spaces

We propose a computationally efficient algorithm that combines compresse...
05/23/2019 ∙ by Chen Tessler, et al. ∙ 0 ∙ share

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Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning

Learning how to act when there are many available actions in each state ...
09/06/2018 ∙ by Tom Zahavy, et al. ∙ 6 ∙ share

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Reward Constrained Policy Optimization

Teaching agents to perform tasks using Reinforcement Learning is no easy...
05/28/2018 ∙ by Chen Tessler, et al. ∙ 0 ∙ share

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Soft-Robust Actor-Critic Policy-Gradient

Robust Reinforcement Learning aims to derive an optimal behavior that ac...
03/11/2018 ∙ by Esther Derman, et al. ∙ 0 ∙ share

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Unicorn: Continual Learning with a Universal, Off-policy Agent

Some real-world domains are best characterized as a single task, but for...
02/22/2018 ∙ by Daniel J. Mankowitz, et al. ∙ 0 ∙ share

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Learning Robust Options

Robust reinforcement learning aims to produce policies that have strong ...
02/09/2018 ∙ by Daniel J. Mankowitz, et al. ∙ 0 ∙ share

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Situationally Aware Options

Hierarchical abstractions, also known as options -- a type of temporally...
11/20/2017 ∙ by Daniel J. Mankowitz, et al. ∙ 0 ∙ share

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Shallow Updates for Deep Reinforcement Learning

Deep reinforcement learning (DRL) methods such as the Deep Q-Network (DQ...
05/21/2017 ∙ by Nir Levine, et al. ∙ 0 ∙ share

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Situational Awareness by Risk-Conscious Skills

Hierarchical Reinforcement Learning has been previously shown to speed u...
10/10/2016 ∙ by Daniel J. Mankowitz, et al. ∙ 0 ∙ share

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Adaptive Skills, Adaptive Partitions (ASAP)

We introduce the Adaptive Skills, Adaptive Partitions (ASAP) framework t...
02/10/2016 ∙ by Daniel J. Mankowitz, et al. ∙ 0 ∙ share

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Iterative Hierarchical Optimization for Misspecified Problems (IHOMP)

For complex, high-dimensional Markov Decision Processes (MDPs), it may b...
02/10/2016 ∙ by Daniel J. Mankowitz, et al. ∙ 0 ∙ share

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CFORB: Circular FREAK-ORB Visual Odometry

We present a novel Visual Odometry algorithm entitled Circular FREAK-ORB...
06/17/2015 ∙ by Daniel J. Mankowitz, et al. ∙ 0 ∙ share

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Bootstrapping Skills

The monolithic approach to policy representation in Markov Decision Proc...
06/11/2015 ∙ by Daniel J. Mankowitz, et al. ∙ 0 ∙ share

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Daniel J. Mankowitz

Featured Co-authors

Shie Mannor

Doina Precup

David Silver

Aviv Tamar

Jerry Li

Martin Riedmiller

Jost Tobias Springenberg

Tom Schaul

Hado van Hasselt

Tom Zahavy

Matteo Hessel

An empirical investigation of the challenges of real-world reinforcement learning

Robust Reinforcement Learning for Continuous Control with Model Misspecification

Action Assembly: Sparse Imitation Learning for Text Based Games with Combinatorial Action Spaces

Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning

Reward Constrained Policy Optimization

Soft-Robust Actor-Critic Policy-Gradient

Unicorn: Continual Learning with a Universal, Off-policy Agent

Learning Robust Options

Situationally Aware Options

Shallow Updates for Deep Reinforcement Learning

Situational Awareness by Risk-Conscious Skills

Adaptive Skills, Adaptive Partitions (ASAP)

Iterative Hierarchical Optimization for Misspecified Problems (IHOMP)

CFORB: Circular FREAK-ORB Visual Odometry

Bootstrapping Skills