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Meta Reinforcement Learning

16 papers with code ·

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ProMP: Proximal Meta-Policy Search

ICLR 2019 learnables/learn2learn

Credit assignment in Meta-reinforcement learning (Meta-RL) is still poorly understood.

META-LEARNING META REINFORCEMENT LEARNING

Learning to reinforcement learn

17 Nov 2016awjuliani/Meta-RL

We unpack these points in a series of seven proof-of-concept experiments, each of which examines a key aspect of deep meta-RL.

META-LEARNING META REINFORCEMENT LEARNING

Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

24 Oct 2019rlworkgroup/metaworld

Therefore, if the aim of these methods is to enable faster acquisition of entirely new behaviors, we must evaluate them on task distributions that are sufficiently broad to enable generalization to new behaviors.

META-LEARNING META REINFORCEMENT LEARNING MULTI-TASK LEARNING

Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables

19 Mar 2019katerakelly/oyster

In our approach, we perform online probabilistic filtering of latent task variables to infer how to solve a new task from small amounts of experience.

EFFICIENT EXPLORATION META REINFORCEMENT LEARNING

Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning

ICLR 2019 iclavera/learning_to_adapt

Although reinforcement learning methods can achieve impressive results in simulation, the real world presents two major challenges: generating samples is exceedingly expensive, and unexpected perturbations or unseen situations cause proficient but specialized policies to fail at test time.

CONTINUOUS CONTROL META-LEARNING META REINFORCEMENT LEARNING POSE ESTIMATION

Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-Learning

CVPR 2019 allenai/savn

In this paper we study the problem of learning to learn at both training and test time in the context of visual navigation.

META-LEARNING META REINFORCEMENT LEARNING VISUAL NAVIGATION

PixelSNAIL: An Improved Autoregressive Generative Model

ICML 2018 neocxi/pixelsnail-public

Autoregressive generative models consistently achieve the best results in density estimation tasks involving high dimensional data, such as images or audio.

#2 best model for Image Generation on CIFAR-10 (NLL Test metric)

DENSITY ESTIMATION IMAGE GENERATION META REINFORCEMENT LEARNING

Meta Reinforcement Learning with Task Embedding and Shared Policy

16 May 2019llan-ml/tesp

Despite significant progress, deep reinforcement learning (RL) suffers from data-inefficiency and limited generalization.

META-LEARNING META REINFORCEMENT LEARNING