166 citations · 714 across the 35 of their papers we have counts for
13 papers · 1 filter
Diffusion Augmented Agents: A Framework for Efficient Exploration and Transfer Learning
Norman Di Palo, Leonard Hasenclever, Jan Humplik +1
We introduce Diffusion Augmented Agents (DAAG), a novel framework that leverages large language models, vision language models, and diffusion models to improve sample efficiency an…
Replay across Experiments: A Natural Extension of Off-Policy RL
Dhruva Tirumala, Thomas Lampe, Jose Enrique Chen +9
Replaying data is a principal mechanism underlying the stability and data efficiency of off-policy reinforcement learning (RL). We present an effective yet simple framework to exte…
Learning Transferable Motor Skills with Hierarchical Latent Mixture Policies
Dushyant Rao, Fereshteh Sadeghi, Leonard Hasenclever +8
For robots operating in the real world, it is desirable to learn reusable behaviours that can effectively be transferred and adapted to numerous tasks and scenarios. We propose an…
Learning Dynamics Models for Model Predictive Agents
Michael Lutter, Leonard Hasenclever, Arunkumar Byravan +5
Model-Based Reinforcement Learning involves learning a \textit{dynamics model} from data, and then using this model to optimise behaviour, most often with an online \textit{planner…
Importance Weighted Policy Learning and Adaptation
Alexandre Galashov, Jakub Sygnowski, Guillaume Desjardins +5
The ability to exploit prior experience to solve novel problems rapidly is a hallmark of biological learning systems and of great practical importance for artificial ones. In the m…
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…
Divide-and-Conquer Monte Carlo Tree Search For Goal-Directed Planning
Giambattista Parascandolo, Lars Buesing, Josh Merel +6
Standard planners for sequential decision making (including Monte Carlo planning, tree search, dynamic programming, etc.) are constrained by an implicit sequential planning assumpt…
Information asymmetry in KL-regularized RL
Alexandre Galashov, Siddhant M. Jayakumar, Leonard Hasenclever +7
Many real world tasks exhibit rich structure that is repeated across different parts of the state space or in time. In this work we study the possibility of leveraging such repeate…
Meta reinforcement learning as task inference
Jan Humplik, Alexandre Galashov, Leonard Hasenclever +3
Humans achieve efficient learning by relying on prior knowledge about the structure of naturally occurring tasks. There is considerable interest in designing reinforcement learning…
Exploiting Hierarchy for Learning and Transfer in KL-regularized RL
Dhruva Tirumala, Hyeonwoo Noh, Alexandre Galashov +6
As reinforcement learning agents are tasked with solving more challenging and diverse tasks, the ability to incorporate prior knowledge into the learning system and to exploit reus…
Neural probabilistic motor primitives for humanoid control
Josh Merel, Leonard Hasenclever, Alexandre Galashov +5
We focus on the problem of learning a single motor module that can flexibly express a range of behaviors for the control of high-dimensional physically simulated humanoids. To do t…
Mix&Match - Agent Curricula for Reinforcement Learning
Wojciech Marian Czarnecki, Siddhant M. Jayakumar, Max Jaderberg +5
We introduce Mix&Match (M&M) - a training framework designed to facilitate rapid and effective learning in RL agents, especially those that would be too slow or too challenging to…
Distributed Bayesian Learning with Stochastic Natural-gradient Expectation Propagation and the Posterior Server
Leonard Hasenclever, Stefan Webb, Thibaut Lienart +4
This paper makes two contributions to Bayesian machine learning algorithms. Firstly, we propose stochastic natural gradient expectation propagation (SNEP), a novel alternative to e…