Meta Reinforcement Learning
40 papers with code • 3 benchmarks • 1 datasets
Benchmarks
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Meta-World Benchmark
Greatest papers with code
learn2learn: A Library for Meta-Learning Research
Meta-learning researchers face two fundamental issues in their empirical work: prototyping and reproducibility.
ProMP: Proximal Meta-Policy Search
learnables/learn2learn
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ICLR 2019
Credit assignment in Meta-reinforcement learning (Meta-RL) is still poorly understood.
Information-theoretic Task Selection for Meta-Reinforcement Learning
maximecb/gym-minigrid
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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-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.
Ranked #1 on
Meta-Learning
on ML10
Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices
Learning a new task often requires both exploring to gather task-relevant information and exploiting this information to solve the task.
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.
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
jonasrothfuss/promp
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ICLR 2018
We consider the problem of exploration in meta reinforcement learning.
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.
PixelSNAIL: An Improved Autoregressive Generative Model
EugenHotaj/pytorch-generative
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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.
