Meta Reinforcement Learning
Leaderboards
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Greatest papers with code
ProMP: Proximal Meta-Policy Search
ICLR 2019
• learnables/learn2learn
• 
Credit assignment in Meta-reinforcement learning (Meta-RL) is still poorly understood.
Learning to reinforcement learn
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-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning
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.
SOTA for Meta-Learning on ML10
META-LEARNING META REINFORCEMENT LEARNING MULTI-TASK LEARNING
Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables
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.
Some Considerations on Learning to Explore via Meta-Reinforcement Learning
ICLR 2018
• jonasrothfuss/promp
• 
We consider the problem of exploration in 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
In this paper we study the problem of learning to learn at both training and test time in the context of 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
• llan-ml/tesp
•
Despite significant progress, deep reinforcement learning (RL) suffers from data-inefficiency and limited generalization.
Meta-Reinforcement Learning of Structured Exploration Strategies
Exploration is a fundamental challenge in reinforcement learning (RL).