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07/21/2023
Towards practical reinforcement learning for tokamak magnetic control
Reinforcement learning (RL) has shown promising results for real-time co...
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05/11/2023
Optimizing Memory Mapping Using Deep Reinforcement Learning
Resource scheduling and allocation is a critical component of many high ...
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11/11/2022
Controlling Commercial Cooling Systems Using Reinforcement Learning
This paper is a technical overview of DeepMind and Google's recent work ...
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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,...
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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...
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02/08/2022
Competition-Level Code Generation with AlphaCode
Programming is a powerful and ubiquitous problem-solving tool. Developin...
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06/18/2021
Active Offline Policy Selection
This paper addresses the problem of policy selection in domains with abu...
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10/20/2020
Robust Constrained Reinforcement Learning for Continuous Control with Model Misspecification
Many real-world physical control systems are required to satisfy constra...
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10/13/2020
Balancing Constraints and Rewards with Meta-Gradient D4PG
Deploying Reinforcement Learning (RL) agents to solve real-world applica...
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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...
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06/18/2019
Robust Reinforcement Learning for Continuous Control with Model Misspecification
We provide a framework for incorporating robustness -- to perturbations ...
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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...
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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 ...
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05/28/2018
Reward Constrained Policy Optimization
Teaching agents to perform tasks using Reinforcement Learning is no easy...
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03/11/2018
Soft-Robust Actor-Critic Policy-Gradient
Robust Reinforcement Learning aims to derive an optimal behavior that ac...
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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...
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02/09/2018
Learning Robust Options
Robust reinforcement learning aims to produce policies that have strong ...
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11/20/2017
Situationally Aware Options
Hierarchical abstractions, also known as options -- a type of temporally...
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05/21/2017
Shallow Updates for Deep Reinforcement Learning
Deep reinforcement learning (DRL) methods such as the Deep Q-Network (DQ...
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10/10/2016
Situational Awareness by Risk-Conscious Skills
Hierarchical Reinforcement Learning has been previously shown to speed u...
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02/10/2016
Adaptive Skills, Adaptive Partitions (ASAP)
We introduce the Adaptive Skills, Adaptive Partitions (ASAP) framework t...
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02/10/2016
Iterative Hierarchical Optimization for Misspecified Problems (IHOMP)
For complex, high-dimensional Markov Decision Processes (MDPs), it may b...
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06/17/2015
CFORB: Circular FREAK-ORB Visual Odometry
We present a novel Visual Odometry algorithm entitled Circular FREAK-ORB...
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06/11/2015
