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
Latest papers
Sequential Decision Making with Expert Demonstrations under Unobserved Heterogeneity
vdblm/ExPerior
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We study the problem of online sequential decision-making given auxiliary demonstrations from experts who made their decisions based on unobserved contextual information.
MAMBA: an Effective World Model Approach for Meta-Reinforcement Learning
zoharri/mamba
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Meta-reinforcement learning (meta-RL) is a promising framework for tackling challenging domains requiring efficient exploration.
Disentangling Policy from Offline Task Representation Learning via Adversarial Data Augmentation
lamda-rl/reda
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Specifically, the objective of adversarial data augmentation is not merely to generate data analogous to offline data distribution; instead, it aims to create adversarial examples designed to confound learned task representations and lead to incorrect task identification.
Generalizable Task Representation Learning for Offline Meta-Reinforcement Learning with Data Limitations
lamda-rl/gentle
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GENTLE employs Task Auto-Encoder~(TAE), which is an encoder-decoder architecture to extract the characteristics of the tasks.
XLand-MiniGrid: Scalable Meta-Reinforcement Learning Environments in JAX
Inspired by the diversity and depth of XLand and the simplicity and minimalism of MiniGrid, we present XLand-MiniGrid, a suite of tools and grid-world environments for meta-reinforcement learning research.
Constrained Meta-Reinforcement Learning for Adaptable Safety Guarantee with Differentiable Convex Programming
Despite remarkable achievements in artificial intelligence, the deployability of learning-enabled systems in high-stakes real-world environments still faces persistent challenges.
Decoupling Meta-Reinforcement Learning with Gaussian Task Contexts and Skills
hehongc/DCMRL
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We propose a framework called decoupled meta-reinforcement learning (DCMRL), which (1) contrastively restricts the learning of task contexts through pulling in similar task contexts within the same task and pushing away different task contexts of different tasks, and (2) utilizes a Gaussian quantization variational autoencoder (GQ-VAE) for clustering the Gaussian distributions of the task contexts and skills respectively, and decoupling the exploration and learning processes of their spaces.
Evolving Reservoirs for Meta Reinforcement Learning
At the developmental scale, we employ these evolved reservoirs to facilitate the learning of a behavioral policy through Reinforcement Learning (RL).
Context Shift Reduction for Offline Meta-Reinforcement Learning
In this paper, we propose a novel approach called Context Shift Reduction for OMRL (CSRO) to address the context shift problem with only offline datasets.
RL$^3$: Boosting Meta Reinforcement Learning via RL inside RL$^2$
Meta reinforcement learning (meta-RL) methods such as RL$^2$ have emerged as promising approaches for learning data-efficient RL algorithms tailored to a given task distribution.
