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
Meta reinforcement learning as task inference
This includes proposals to learn the learning algorithm itself, an idea also known as meta learning.
Loaded DiCE: Trading off Bias and Variance in Any-Order Score Function Estimators for Reinforcement Learning
Gradient-based methods for optimisation of objectives in stochastic settings with unknown or intractable dynamics require estimators of derivatives.
Loaded DiCE: Trading off Bias and Variance in Any-Order Score Function Gradient Estimators for Reinforcement Learning
Gradient-based methods for optimisation of objectives in stochastic settings with unknown or intractable dynamics require estimators of derivatives.
Meta Reinforcement Learning with Autonomous Inference of Subtask Dependencies
srsohn/msgi
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ICLR 2020
We propose and address a novel few-shot RL problem, where a task is characterized by a subtask graph which describes a set of subtasks and their dependencies that are unknown to the agent.
On the Convergence Theory of Debiased Model-Agnostic Meta-Reinforcement Learning
kristian-georgiev/sgmrl
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NeurIPS 2021
We consider Model-Agnostic Meta-Learning (MAML) methods for Reinforcement Learning (RL) problems, where the goal is to find a policy using data from several tasks represented by Markov Decision Processes (MDPs) that can be updated by one step of stochastic policy gradient for the realized MDP.
Evolving Inborn Knowledge For Fast Adaptation in Dynamic POMDP Problems
dlpbc/penn-a
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Rapid online adaptation to changing tasks is an important problem in machine learning and, recently, a focus of meta-reinforcement learning.
Exchangeable Models in Meta Reinforcement Learning
irakorshunova/bruno-sac
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ICML Workshop LifelongML 2020
One recent approach to meta reinforcement learning (meta-RL) is to integrate models for task inference with models for control.
MetaCURE: Meta Reinforcement Learning with Empowerment-Driven Exploration
Meta reinforcement learning (meta-RL) extracts knowledge from previous tasks and achieves fast adaptation to new tasks.
Model-based Adversarial Meta-Reinforcement Learning
LinZichuan/AdMRL
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NeurIPS 2020
When the test task distribution is different from the training task distribution, the performance may degrade significantly.
Fast Adaptive Task Offloading in Edge Computing based on Meta Reinforcement Learning
linkpark/metarl-offloading
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Recently, many deep reinforcement learning (DRL) based methods have been proposed to learn offloading policies through interacting with the MEC environment that consists of UE, wireless channels, and MEC hosts.
