439 citations · 1.3k across the 23 of their papers we have counts for
4 papers · 1 filter
Balancing Constraints and Rewards with Meta-Gradient D4PG
Dan A. Calian, Daniel J. Mankowitz, Tom Zahavy +4
Deploying Reinforcement Learning (RL) agents to solve real-world applications often requires satisfying complex system constraints. Often the constraint thresholds are incorrectly…
Meta-Gradient Reinforcement Learning with an Objective Discovered Online
Zhongwen Xu, Hado van Hasselt, Matteo Hessel +3
Deep reinforcement learning includes a broad family of algorithms that parameterise an internal representation, such as a value function or policy, by a deep neural network. Each a…
Discovering Reinforcement Learning Algorithms
Junhyuk Oh, Matteo Hessel, Wojciech M. Czarnecki +4
Reinforcement learning (RL) algorithms update an agent's parameters according to one of several possible rules, discovered manually through years of research. Automating the discov…
A Self-Tuning Actor-Critic Algorithm
Tom Zahavy, Zhongwen Xu, Vivek Veeriah +5
Reinforcement learning algorithms are highly sensitive to the choice of hyperparameters, typically requiring significant manual effort to identify hyperparameters that perform well…