Luisa Zintgraf | DeepAI

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∙ 01/19/2023

A Survey of Meta-Reinforcement Learning

While deep reinforcement learning (RL) has fueled multiple high-profile ...

0 Jacob Beck, et al. ∙

∙ 06/26/2022

Generalized Beliefs for Cooperative AI

Self-play is a common paradigm for constructing solutions in Markov game...

2 Darius Muglich, et al. ∙

∙ 12/01/2021

On the Practical Consistency of Meta-Reinforcement Learning Algorithms

Consistency is the theoretical property of a meta learning algorithm tha...

0 Zheng Xiong, et al. ∙

∙ 07/17/2021

Implicit Communication as Minimum Entropy Coupling

In many common-payoff games, achieving good performance requires players...

3 Samuel Sokota, et al. ∙

∙ 06/21/2021

Optimizing piano practice with a utility-based scaffold

A typical part of learning to play the piano is the progression through ...

0 Alexandra Moringen, et al. ∙

∙ 04/17/2021

A Self-Supervised Auxiliary Loss for Deep RL in Partially Observable Settings

In this work we explore an auxiliary loss useful for reinforcement learn...

0 Eltayeb Ahmed, et al. ∙

∙ 04/08/2021

ORBIT: A Real-World Few-Shot Dataset for Teachable Object Recognition

Object recognition has made great advances in the last decade, but predo...

0 Daniela Massiceti, et al. ∙

∙ 01/11/2021

Deep Interactive Bayesian Reinforcement Learning via Meta-Learning

Agents that interact with other agents often do not know a priori what t...

0 Luisa Zintgraf, et al. ∙

∙ 10/02/2020

Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning

Meta-learning is a powerful tool for learning policies that can adapt ef...

4 Luisa Zintgraf, et al. ∙

∙ 11/29/2019

VIABLE: Fast Adaptation via Backpropagating Learned Loss

In few-shot learning, typically, the loss function which is applied at t...

10 Leo Feng, et al. ∙

∙ 10/18/2019

VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning

Trading off exploration and exploitation in an unknown environment is ke...

66 Luisa Zintgraf, et al. ∙

∙ 06/06/2018

Deep Variational Reinforcement Learning for POMDPs

Many real-world sequential decision making problems are partially observ...

2 Maximilian Igl, et al. ∙

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