
An investigation of modelfree planning
The field of reinforcement learning (RL) is facing increasingly challeng...
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Normalizing Flows on Tori and Spheres
Normalizing flows are a powerful tool for building expressive distributi...
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Targeted free energy estimation via learned mappings
Free energy perturbation (FEP) was proposed by Zwanzig more than six dec...
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ImaginationAugmented Agents for Deep Reinforcement Learning
We introduce ImaginationAugmented Agents (I2As), a novel architecture f...
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Learning modelbased planning from scratch
Conventional wisdom holds that modelbased planning is a powerful approa...
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Recurrent Environment Simulators
Models that can simulate how environments change in response to actions ...
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Learning and Querying Fast Generative Models for Reinforcement Learning
A key challenge in modelbased reinforcement learning (RL) is to synthes...
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The Mechanics of nPlayer Differentiable Games
The cornerstone underpinning deep learning is the guarantee that gradien...
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Woulda, Coulda, Shoulda: CounterfactuallyGuided Policy Search
Learning policies on data synthesized by models can in principle quench ...
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Towards a Definition of Disentangled Representations
How can intelligent agents solve a diverse set of tasks in a dataeffici...
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Differentiable Game Mechanics
Deep learning is built on the foundational guarantee that gradient desce...
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Hamiltonian Generative Networks
The Hamiltonian formalism plays a central role in classical and quantum ...
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Equivariant Hamiltonian Flows
This paper introduces equivariant hamiltonian flows, a method for learni...
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Automated curricula through settersolver interactions
Reinforcement learning algorithms use correlations between policies and ...
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Equivariant flowbased sampling for lattice gauge theory
We define a class of machinelearned flowbased sampling algorithms for ...
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Sebastien Racanière
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