Meta Reinforcement Learning

Benchmarks archive 2025-07-28

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Libraries

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Datasets archive 2025-07-28

2 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

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Most implemented papers archive 2025-07-28

30 shown of 103 papers with code (278 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning
  24 Oct 2019 9 repositories listed Syntology ran 0 of 9 samples · 9 unverified
  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.
• Learning to reinforcement learn
  17 Nov 2016 9 repositories listed Syntology ran 3 of 15 samples · 12 unverified · 12 pointer-only (licence)
  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
  19 Mar 2019 7 repositories listed Syntology ran 6 of 9 samples · 3 unverified · 1 pointer-only (licence)
  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
  3 Mar 2018 7 repositories listed
  We consider the problem of exploration in meta reinforcement learning.
• ProMP: Proximal Meta-Policy Search
  16 Oct 2018 6 repositories listed Syntology ran 0 of 4 samples · 4 unverified
  Credit assignment in Meta-reinforcement learning (Meta-RL) is still poorly understood.
• PixelSNAIL: An Improved Autoregressive Generative Model
  28 Dec 2017 6 repositories listed Syntology ran 6 of 7 samples · 1 unverified · 3 pointer-only (licence)
  Autoregressive generative models consistently achieve the best results in density estimation tasks involving high dimensional data, such as images or audio.
• Diversity is All You Need: Learning Skills without a Reward Function
  16 Feb 2018 4 repositories listed Syntology ran 5 of 11 samples · 6 unverified
  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.
• Evolving Reservoirs for Meta Reinforcement Learning
  9 Dec 2023 3 repositories listed
  At the developmental scale, we employ these evolved reservoirs to facilitate the learning of a behavioral policy through Reinforcement Learning (RL).
• Meta-World+: An Improved, Standardized, RL Benchmark
  16 May 2025 2 repositories listed Syntology ran 1 of 2 samples · 1 unverified
  Meta-World is widely used for evaluating multi-task and meta-reinforcement learning agents, which are challenged to master diverse skills simultaneously.
• XLand-MiniGrid: Scalable Meta-Reinforcement Learning Environments in JAX
  19 Dec 2023 2 repositories listed
  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.
• Context Meta-Reinforcement Learning via Neuromodulation
  30 Oct 2021 2 repositories listed Syntology ran 2 of 5 samples · 3 unverified
  Meta-reinforcement learning (meta-RL) algorithms enable agents to adapt quickly to tasks from few samples in dynamic environments.
• learn2learn: A Library for Meta-Learning Research
  27 Aug 2020 2 repositories listed Syntology ran 2 of 2 samples · 0 unverified
  Meta-learning researchers face two fundamental issues in their empirical work: prototyping and reproducibility.
• Offline Meta-Reinforcement Learning with Advantage Weighting
  13 Aug 2020 2 repositories listed Syntology ran 1 of 5 samples · 4 unverified · 5 pointer-only (licence)
  That is, in offline meta-RL, we meta-train on fixed, pre-collected data from several tasks in order to adapt to a new task with a very small amount (less than 5 trajectories) of data from the new task.
• Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices
  6 Aug 2020 2 repositories listed
  Learning a new task often requires both exploring to gather task-relevant information and exploiting this information to solve the task.
• Learning Robust State Abstractions for Hidden-Parameter Block MDPs
  14 Jul 2020 2 repositories listed
  Further, we provide transfer and generalization bounds based on task and state similarity, along with sample complexity bounds that depend on the aggregate number of samples across tasks, rather than the number of…
• Model-Based Meta-Reinforcement Learning for Flight with Suspended Payloads
  23 Apr 2020 2 repositories listed
  Our experiments demonstrate that our online adaptation approach outperforms non-adaptive methods on a series of challenging suspended payload transportation tasks.
• Meta-Q-Learning
  30 Sep 2019 2 repositories listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  This paper introduces Meta-Q-Learning (MQL), a new off-policy algorithm for meta-Reinforcement Learning (meta-RL).
• Meta Reinforcement Learning with Task Embedding and Shared Policy
  16 May 2019 2 repositories listed
  Despite significant progress, deep reinforcement learning (RL) suffers from data-inefficiency and limited generalization.
• Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-Learning
  3 Dec 2018 2 repositories listed Syntology ran 1 of 4 samples · 3 unverified
  In this paper we study the problem of learning to learn at both training and test time in the context of visual navigation.
• Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning
  30 Mar 2018 2 repositories listed
  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…
• Meta-Reinforcement Learning of Structured Exploration Strategies
  20 Feb 2018 2 repositories listed
  Exploration is a fundamental challenge in reinforcement learning (RL).
• Learning Task Belief Similarity with Latent Dynamics for Meta-Reinforcement Learning
  24 Jun 2025 1 repository listed
  Meta-reinforcement learning requires utilizing prior task distribution information obtained during exploration to rapidly adapt to unknown tasks.
• Bayesian Meta-Reinforcement Learning with Laplace Variational Recurrent Networks
  24 May 2025 1 repository listed
  Meta-reinforcement learning trains a single reinforcement learning agent on a distribution of tasks to quickly generalize to new tasks outside of the training set at test time.
• Task-Aware Virtual Training: Enhancing Generalization in Meta-Reinforcement Learning for Out-of-Distribution Tasks
  5 Feb 2025 1 repository listed Syntology ran 3 of 3 samples · 0 unverified
  Meta reinforcement learning aims to develop policies that generalize to unseen tasks sampled from a task distribution.
• Coreset-Based Task Selection for Sample-Efficient Meta-Reinforcement Learning
  4 Feb 2025 1 repository listed
  To address this, we propose a coreset-based task selection approach that selects a weighted subset of tasks based on how diverse they are in gradient space, prioritizing the most informative and diverse tasks.
• Entropy Regularized Task Representation Learning for Offline Meta-Reinforcement Learning
  19 Dec 2024 1 repository listed
  Context-based approaches utilize a history of state-action-reward transitions -- referred to as the context -- to infer representations of the current task, and then condition the agent, i.
• Mind the Gap: Towards Generalizable Autonomous Penetration Testing via Domain Randomization and Meta-Reinforcement Learning
  5 Dec 2024 1 repository listed
  GAP introduces a Real-to-Sim-to-Real pipeline that (a) enables end-to-end policy learning in unknown real environments while constructing realistic simulations; (b) improves agents' generalization ability by leveraging…
• AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers
  17 Nov 2024 1 repository listed Syntology ran 2 of 10 samples · 8 unverified · 1 pointer-only (licence)
  Language models trained on diverse datasets unlock generalization by in-context learning.
• Enabling Adaptive Agent Training in Open-Ended Simulators by Targeting Diversity
  7 Nov 2024 1 repository listed Syntology ran 0 of 5 samples · 5 unverified
  The wider application of end-to-end learning methods to embodied decision-making domains remains bottlenecked by their reliance on a superabundance of training data representative of the target domain.
• FALCON: Feedback-driven Adaptive Long/short-term memory reinforced Coding Optimization system
  28 Oct 2024 1 repository listed
  To tackle these challenges and improve the code generation performance for automated programming systems, we propose Feedback-driven Adaptive Long/short-term memory reinforced Coding Optimization (i.

Syntology lines on 15 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.