Daniel J. Mankowitz

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Technion

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

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∙ 04/19/2022

COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimation

We consider the offline constrained reinforcement learning (RL) problem,...

Jongmin Lee, et al. ∙

∙ 02/14/2022

MuZero with Self-competition for Rate Control in VP9 Video Compression

Video streaming usage has seen a significant rise as entertainment, educ...

Amol Mandhane, et al. ∙

∙ 02/08/2022

Competition-Level Code Generation with AlphaCode

Programming is a powerful and ubiquitous problem-solving tool. Developin...

Yujia Li, et al. ∙

∙ 06/18/2021

Active Offline Policy Selection

This paper addresses the problem of policy selection in domains with abu...

Ksenia Konyushkova, et al. ∙

∙ 10/20/2020

Robust Constrained Reinforcement Learning for Continuous Control with Model Misspecification

Many real-world physical control systems are required to satisfy constra...

Daniel J. Mankowitz, et al. ∙

∙ 10/13/2020

Balancing Constraints and Rewards with Meta-Gradient D4PG

Deploying Reinforcement Learning (RL) agents to solve real-world applica...

Dan A. Calian, et al. ∙

∙ 03/24/2020

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

Reinforcement learning (RL) has proven its worth in a series of artifici...

Gabriel Dulac-Arnold, et al. ∙

∙ 06/18/2019

Robust Reinforcement Learning for Continuous Control with Model Misspecification

We provide a framework for incorporating robustness -- to perturbations ...

Daniel J. Mankowitz, et al. ∙

∙ 05/23/2019

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

We propose a computationally efficient algorithm that combines compresse...

Chen Tessler, et al. ∙

∙ 09/06/2018

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

Learning how to act when there are many available actions in each state ...

Tom Zahavy, et al. ∙

∙ 05/28/2018

Reward Constrained Policy Optimization

Teaching agents to perform tasks using Reinforcement Learning is no easy...

Chen Tessler, et al. ∙

∙ 03/11/2018

Soft-Robust Actor-Critic Policy-Gradient

Robust Reinforcement Learning aims to derive an optimal behavior that ac...

Esther Derman, et al. ∙

∙ 02/22/2018

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

Some real-world domains are best characterized as a single task, but for...

Daniel J. Mankowitz, et al. ∙

∙ 02/09/2018

Learning Robust Options

Robust reinforcement learning aims to produce policies that have strong ...

Daniel J. Mankowitz, et al. ∙

∙ 11/20/2017

Situationally Aware Options

Hierarchical abstractions, also known as options -- a type of temporally...

Daniel J. Mankowitz, et al. ∙

∙ 05/21/2017

Shallow Updates for Deep Reinforcement Learning

Deep reinforcement learning (DRL) methods such as the Deep Q-Network (DQ...

Nir Levine, et al. ∙

∙ 10/10/2016

Situational Awareness by Risk-Conscious Skills

Hierarchical Reinforcement Learning has been previously shown to speed u...

Daniel J. Mankowitz, et al. ∙

∙ 02/10/2016

Adaptive Skills, Adaptive Partitions (ASAP)

We introduce the Adaptive Skills, Adaptive Partitions (ASAP) framework t...

Daniel J. Mankowitz, et al. ∙

∙ 02/10/2016

Iterative Hierarchical Optimization for Misspecified Problems (IHOMP)

For complex, high-dimensional Markov Decision Processes (MDPs), it may b...

Daniel J. Mankowitz, et al. ∙

∙ 06/17/2015

CFORB: Circular FREAK-ORB Visual Odometry

We present a novel Visual Odometry algorithm entitled Circular FREAK-ORB...

Daniel J. Mankowitz, et al. ∙

∙ 06/11/2015

Bootstrapping Skills

The monolithic approach to policy representation in Markov Decision Proc...

Daniel J. Mankowitz, et al. ∙