Author name cluster

Leonard Hasenclever

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12 papers

2 author rows

ICLR Conference 2024 Conference Paper

Replay across Experiments: A Natural Extension of Off-Policy RL

Dhruva Tirumala
Thomas Lampe
José Enrique Chen
Tuomas Haarnoja
Sandy Han Huang
Guy Lever
Ben Moran
Tim Hertweck

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 extend the use of replays across multiple experiments, minimally adapting the RL workflow for sizeable improvements in controller performance and research iteration times. At its core, Replay across Experiments (RaE) involves reusing experience from previous experiments to improve exploration and bootstrap learning while reducing required changes to a minimum in comparison to prior work. We empirically show benefits across a number of RL algorithms and challenging control domains spanning both locomotion and manipulation, including hard exploration tasks from egocentric vision. Through comprehensive ablations, we demonstrate robustness to the quality and amount of data available and various hyperparameter choices. Finally, we discuss how our approach can be applied more broadly across research life cycles and can increase resilience by reloading data across random seeds or hyperparameter variations.

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ICRA Conference 2023 Conference Paper

NeRF2Real: Sim2real Transfer of Vision-guided Bipedal Motion Skills using Neural Radiance Fields

Arunkumar Byravan
Jan Humplik
Leonard Hasenclever
Arthur Brussee
Francesco Nori
Tuomas Haarnoja
Ben Moran
Steven Bohez

We present a system for applying sim2real approaches to “in the wild” scenes with realistic visuals, and to policies which rely on active perception using RGB cameras. Given a short video of a static scene collected using a generic phone, we learn the scene's contact geometry and a function for novel view synthesis using a Neural Radiance Field (NeRF). We augment the NeRF rendering of the static scene by overlaying the rendering of other dynamic objects (e. g. the robot's own body, a ball). A simulation is then created using the rendering engine in a physics simulator which computes contact dynamics from the static scene geometry (estimated from the NeRF vol-ume density) and the dynamic objects' geometry and physical properties (assumed known). We demonstrate that we can use this simulation to learn vision-based whole body navigation and ball pushing policies for a 20 degree-of-freedom humanoid robot with an actuated head-mounted RGB camera, and we successfully transfer these policies to a real robot.

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JMLR Journal 2022 Journal Article

Behavior Priors for Efficient Reinforcement Learning

Dhruva Tirumala
Alexandre Galashov
Hyeonwoo Noh
Leonard Hasenclever
Razvan Pascanu
Jonathan Schwarz
Guillaume Desjardins
Wojciech Marian Czarnecki

As we deploy reinforcement learning agents to solve increasingly challenging problems, methods that allow us to inject prior knowledge about the structure of the world and effective solution strategies becomes increasingly important. In this work we consider how information and architectural constraints can be combined with ideas from the probabilistic modeling literature to learn behavior priors that capture the common movement and interaction patterns that are shared across a set of related tasks or contexts. For example the day-to day behavior of humans comprises distinctive locomotion and manipulation patterns that recur across many different situations and goals. We discuss how such behavior patterns can be captured using probabilistic trajectory models and how these can be integrated effectively into reinforcement learning schemes, e.g. to facilitate multi-task and transfer learning. We then extend these ideas to latent variable models and consider a formulation to learn hierarchical priors that capture different aspects of the behavior in reusable modules. We discuss how such latent variable formulations connect to related work on hierarchical reinforcement learning (HRL) and mutual information and curiosity based objectives, thereby offering an alternative perspective on existing ideas. We demonstrate the effectiveness of our framework by applying it to a range of simulated continuous control domains, videos of which can be found at the following url: https://sites.google.com/view/behavior-priors. [abs] [ pdf ][ bib ] &copy JMLR 2022. ( edit, beta )

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ICLR Conference 2022 Conference Paper

Evaluating Model-Based Planning and Planner Amortization for Continuous Control

Arunkumar Byravan
Leonard Hasenclever
Piotr Trochim
Mehdi Mirza
Alessandro Davide Ialongo
Yuval Tassa
Jost Tobias Springenberg
Abbas Abdolmaleki

There is a widespread intuition that model-based control methods should be able to surpass the data efficiency of model-free approaches. In this paper we attempt to evaluate this intuition on various challenging locomotion tasks. We take a hybrid approach, combining model predictive control (MPC) with a learned model and model-free policy learning; the learned policy serves as a proposal for MPC. We show that MPC with learned proposals and models (trained on the fly or transferred from related tasks) can significantly improve performance and data efficiency with respect to model-free methods. However, we find that well-tuned model-free agents are strong baselines even for high DoF control problems. Finally, we show that it is possible to distil a model-based planner into a policy that amortizes the planning computation without any loss of performance.

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IROS Conference 2022 Conference Paper

Learning Coordinated Terrain-Adaptive Locomotion by Imitating a Centroidal Dynamics Planner

Philemon Brakel
Steven Bohez
Leonard Hasenclever
Nicolas Heess
Konstantinos Bousmalis

We propose a simple imitation learning procedure for learning locomotion controllers that can walk over very challenging terrains. We use trajectory optimization (TO) to produce a large dataset of trajectories over procedurally generated terrains and use Reinforcement Learning (RL) to imitate these trajectories. We demonstrate with a realistic model of the ANYmal robot that the learned controllers transfer to unseen terrains and provide an effective initialization for fine-tuning on challenging terrains that require exteroception and precise foot placements. Our setup combines TO and RL in a simple fashion that overcomes the computational limitations and need for a robust tracking controller of the former and the exploration and reward-tuning difficulties of the latter.

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ICLR Conference 2022 Conference Paper

Learning transferable motor skills with hierarchical latent mixture policies

Dushyant Rao
Fereshteh Sadeghi
Leonard Hasenclever
Markus Wulfmeier
Martina Zambelli
Giulia Vezzani
Dhruva Tirumala
Yusuf Aytar

For robots operating in the real world, it is desirable to learn reusable abstract behaviours that can effectively be transferred across numerous tasks and scenarios. We propose an approach to learn skills from data using a hierarchical mixture latent variable model. Our method exploits a multi-level hierarchy of both discrete and continuous latent variables, to model a discrete set of abstract high-level behaviours while allowing for variance in how they are executed. We demonstrate in manipulation domains that the method can effectively cluster offline data into distinct, executable behaviours, while retaining the flexibility of a continuous latent variable model. The resulting skills can be transferred to new tasks, unseen objects, and from state to vision-based policies, yielding significantly better sample efficiency and asymptotic performance compared to existing skill- and imitation-based methods. We also perform further analysis showing how and when the skills are most beneficial: they encourage directed exploration to cover large regions of the state space relevant to the task, making them most effective in challenging sparse-reward settings.

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ICML Conference 2020 Conference Paper

A distributional view on multi-objective policy optimization

Abbas Abdolmaleki
Sandy Han Huang
Leonard Hasenclever
Michael Neunert
H. Francis Song
Martina Zambelli
Murilo F. Martins
Nicolas Heess

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 practitioners to express numerical preferences over objectives in their native units. In this paper we propose a novel algorithm for multi-objective reinforcement learning that enables setting desired preferences for objectives in a scale-invariant way. We propose to learn an action distribution for each objective, and we use supervised learning to fit a parametric policy to a combination of these distributions. We demonstrate the effectiveness of our approach on challenging high-dimensional real and simulated robotics tasks, and show that setting different preferences in our framework allows us to trace out the space of nondominated solutions.

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ICML Conference 2020 Conference Paper

CoMic: Complementary Task Learning & Mimicry for Reusable Skills

Leonard Hasenclever
Fabio Pardo
Raia Hadsell
Nicolas Heess
Josh Merel

Learning to control complex bodies and reuse learned behaviors is a longstanding challenge in continuous control. We study the problem of learning reusable humanoid skills by imitating motion capture data and joint training with complementary tasks. We show that it is possible to learn reusable skills through reinforcement learning on 50 times more motion capture data than prior work. We systematically compare a variety of different network architectures across different data regimes both in terms of imitation performance as well as transfer to challenging locomotion tasks. Finally we show that it is possible to interleave the motion capture tracking with training on complementary tasks, enriching the resulting skill space, and enabling the reuse of skills not well covered by the motion capture data such as getting up from the ground or catching a ball.

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AAMAS Conference 2019 Conference Paper

Observational Learning by Reinforcement Learning

Diana Borsa
Nicolas Heess
Bilal Piot
Siqi Liu
Leonard Hasenclever
Remi Munos
Olivier Pietquin

Observational learning is a type of learning that occurs as a function of observing, retaining and possibly imitating the behaviour of another agent. It is a core mechanism appearing in various instances of social learning and has been found to be employed in several intelligent species, including humans. In this paper, we investigate to what extent the explicit modelling of other agents is necessary to achieve observational learning through machine learning. Especially, we argue that observational learning can emerge from pure Reinforcement Learning (RL), potentially coupled with memory. Through simple scenarios, we demonstrate that an RL agent can leverage the information provided by the observations of an other agent performing a task in a shared environment. The other agent is only observed through the e�ect of its actions on the environment and never explicitly modeled. Two key aspects are borrowed from observational learning: i) the observer behaviour needs to change as a result of viewing a ’teacher’ (another agent) and ii) the observer needs to be motivated somehow to engage in making use of the other agent’s behaviour. The later is naturally modeled by RL, by correlating the learning agent’s reward with the teacher agent’s behaviour.

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ICML Conference 2018 Conference Paper

Mix & Match Agent Curricula for Reinforcement Learning

Wojciech Czarnecki 0001
Siddhant M. Jayakumar
Max Jaderberg
Leonard Hasenclever
Yee Whye Teh
Nicolas Heess
Simon Osindero
Razvan Pascanu

We introduce Mix and match (M&M) – a training framework designed to facilitate rapid and effective learning in RL agents that would be too slow or too challenging to train otherwise. The key innovation is a procedure that allows us to automatically form a curriculum over agents. Through such a curriculum we can progressively train more complex agents by, effectively, bootstrapping from solutions found by simpler agents. In contradistinction to typical curriculum learning approaches, we do not gradually modify the tasks or environments presented, but instead use a process to gradually alter how the policy is represented internally. We show the broad applicability of our method by demonstrating significant performance gains in three different experimental setups: (1) We train an agent able to control more than 700 actions in a challenging 3D first-person task; using our method to progress through an action-space curriculum we achieve both faster training and better final performance than one obtains using traditional methods. (2) We further show that M&M can be used successfully to progress through a curriculum of architectural variants defining an agents internal state. (3) Finally, we illustrate how a variant of our method can be used to improve agent performance in a multitask setting.

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UAI Conference 2018 Conference Paper

Sylvester Normalizing Flows for Variational Inference

Rianne van den Berg
Leonard Hasenclever
Jakub M. Tomczak
Max Welling

Variational inference relies on flexible approximate posterior distributions. Normalizing flows provide a general recipe to construct flexible variational posteriors. We introduce Sylvester normalizing flows, which can be seen as a generalization of planar flows. Sylvester normalizing flows remove the well-known single-unit bottleneck from planar flows, making a single transformation much more flexible. We compare the performance of Sylvester normalizing flows against planar flows and inverse autoregressive flows and demonstrate that they compare favorably on several datasets.

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JMLR Journal 2017 Journal Article

Distributed Bayesian Learning with Stochastic Natural Gradient Expectation Propagation and the Posterior Server

Leonard Hasenclever
Stefan Webb
Thibaut Lienart
Sebastian Vollmer
Balaji Lakshminarayanan
Charles Blundell
Yee Whye Teh

This paper makes two contributions to Bayesian machine learning algorithms. Firstly, we propose stochastic natural gradient expectation propagation (SNEP), a novel alternative to expectation propagation (EP), a popular variational inference algorithm. SNEP is a black box variational algorithm, in that it does not require any simplifying assumptions on the distribution of interest, beyond the existence of some Monte Carlo sampler for estimating the moments of the EP tilted distributions. Further, as opposed to EP which has no guarantee of convergence, SNEP can be shown to be convergent, even when using Monte Carlo moment estimates. Secondly, we propose a novel architecture for distributed Bayesian learning which we call the posterior server. The posterior server allows scalable and robust Bayesian learning in cases where a data set is stored in a distributed manner across a cluster, with each compute node containing a disjoint subset of data. An independent Monte Carlo sampler is run on each compute node, with direct access only to the local data subset, but which targets an approximation to the global posterior distribution given all data across the whole cluster. This is achieved by using a distributed asynchronous implementation of SNEP to pass messages across the cluster. We demonstrate SNEP and the posterior server on distributed Bayesian learning of logistic regression and neural networks. [abs] [ pdf ][ bib ] &copy JMLR 2017. ( edit, beta )

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