Meta Reinforcement Learning

40 papers with code • 3 benchmarks • 1 datasets

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Greatest papers with code

learn2learn: A Library for Meta-Learning Research

learnables/learn2learn 27 Aug 2020

Meta-learning researchers face two fundamental issues in their empirical work: prototyping and reproducibility.

Few-Shot Learning Meta Reinforcement Learning

ProMP: Proximal Meta-Policy Search

learnables/learn2learn ICLR 2019

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

Meta-Learning Meta Reinforcement Learning

Information-theoretic Task Selection for Meta-Reinforcement Learning

maximecb/gym-minigrid NeurIPS 2020

In Meta-Reinforcement Learning (meta-RL) an agent is trained on a set of tasks to prepare for and learn faster in new, unseen, but related tasks.

Meta Reinforcement Learning

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

rlworkgroup/metaworld 24 Oct 2019

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 +1

Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices

maximecb/gym-miniworld 6 Aug 2020

Learning a new task often requires both exploring to gather task-relevant information and exploiting this information to solve the task.

Meta Reinforcement Learning Visual Navigation

Learning to reinforcement learn

awjuliani/Meta-RL 17 Nov 2016

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

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

katerakelly/oyster 19 Mar 2019

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

iclavera/learning_to_adapt ICLR 2019

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 +3

PixelSNAIL: An Improved Autoregressive Generative Model

EugenHotaj/pytorch-generative ICML 2018

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

Density Estimation Image Generation +1