521 citations · 1.2k across the 41 of their papers we have counts for
7 papers · 1 filter
"What, not how": Solving an under-actuated insertion task from scratch
Giulia Vezzani, Michael Neunert, Markus Wulfmeier +7
Robot manipulation requires a complex set of skills that need to be carefully combined and coordinated to solve a task. Yet, most ReinforcementLearning (RL) approaches in robotics…
Local Search for Policy Iteration in Continuous Control
Jost Tobias Springenberg, Nicolas Heess, Daniel Mankowitz +10
We present an algorithm for local, regularized, policy improvement in reinforcement learning (RL) that allows us to formulate model-based and model-free variants in a single framew…
Data-efficient Hindsight Off-policy Option Learning
Markus Wulfmeier, Dushyant Rao, Roland Hafner +8
We introduce Hindsight Off-policy Options (HO2), a data-efficient option learning algorithm. Given any trajectory, HO2 infers likely option choices and backpropagates through the d…
Acme: A Research Framework for Distributed Reinforcement Learning
Matthew W. Hoffman, Bobak Shahriari, John Aslanides +36
Deep reinforcement learning (RL) has led to many recent and groundbreaking advances. However, these advances have often come at the cost of both increased scale in the underlying a…
A Distributional View on Multi-Objective Policy Optimization
Abbas Abdolmaleki, Sandy H. Huang, Leonard Hasenclever +7
Many real-world problems require trading off multiple competing objectives. However, these objectives are often in different units and/or scales, which can make it challenging for…
Keep Doing What Worked: Behavioral Modelling Priors for Offline Reinforcement Learning
Noah Y. Siegel, Jost Tobias Springenberg, Felix Berkenkamp +6
Off-policy reinforcement learning algorithms promise to be applicable in settings where only a fixed data-set (batch) of environment interactions is available and no new experience…