Meta Reinforcement Learning
88 papers with code • 2 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Meta Reinforcement Learning
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Libraries
Use these libraries to find Meta Reinforcement Learning models and implementations
Meta-World Benchmark
Most implemented papers
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.
Some Considerations on Learning to Explore via Meta-Reinforcement Learning
episodeyang/e-maml
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ICLR 2018
We consider the problem of exploration in meta reinforcement learning.
Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables
katerakelly/oyster
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ICLR Workshop LLD 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.
PixelSNAIL: An Improved Autoregressive Generative Model
neocxi/pixelsnail-public
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ICML 2018
Autoregressive generative models consistently achieve the best results in density estimation tasks involving high dimensional data, such as images or audio.
ProMP: Proximal Meta-Policy Search
jonasrothfuss/promp
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ICLR 2019
Credit assignment in Meta-reinforcement learning (Meta-RL) is still poorly understood.
Diversity is All You Need: Learning Skills without a Reward Function
navneet-nmk/Hierarchical-Meta-Reinforcement-Learning
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ICLR 2019
On a variety of simulated robotic tasks, we show that this simple objective results in the unsupervised emergence of diverse skills, such as walking and jumping.
Meta-Reinforcement Learning of Structured Exploration Strategies
russellmendonca/maesn_suite
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NeurIPS 2018
Exploration is a fundamental challenge in reinforcement learning (RL).
Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning
iclavera/learning_to_adapt
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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.
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.
