Cover Image

Goal-Conditioned Agents that Learn Everything All at Once

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Matthews, Michael, Jackson, Matthew, Beukman, Michael, Foster, Thomas, Letcher, Alistair, Fujimoto, Scott, Colas, Cédric, Foerster, Jakob
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Kinetix: Investigating the Training of General Agents through Open-Ended Physics-Based Control Tasks
by: Matthews, Michael, et al.
Published: (2024)

JaxUED: A simple and useable UED library in Jax
by: Coward, Samuel, et al.
Published: (2024)

JaxLife: An Open-Ended Agentic Simulator
by: Lu, Chris, et al.
Published: (2024)

Refining Minimax Regret for Unsupervised Environment Design
by: Beukman, Michael, et al.
Published: (2024)

Autotelic Agents with Intrinsically Motivated Goal-Conditioned Reinforcement Learning: a Short Survey
by: Colas, Cédric, et al.
Published: (2020)

Abstraction for Offline Goal-Conditioned Reinforcement Learning
by: Wibault, Clarisse, et al.
Published: (2026)

Hierarchical Behaviour Spaces
by: Matthews, Michael Tryfan, et al.
Published: (2026)

An Optimisation Framework for Unsupervised Environment Design
by: Monette, Nathan, et al.
Published: (2025)

No Regrets: Investigating and Improving Regret Approximations for Curriculum Discovery
by: Rutherford, Alexander, et al.
Published: (2024)

Policy-Guided Diffusion
by: Jackson, Matthew Thomas, et al.
Published: (2024)

Scalable Option Learning in High-Throughput Environments
by: Henaff, Mikael, et al.
Published: (2025)

Everything, Everywhere, All at Once: Is Mechanistic Interpretability Identifiable?
by: Méloux, Maxime, et al.
Published: (2025)

Learning Multi-Agent Communication with Contrastive Learning
by: Lo, Yat Long, et al.
Published: (2023)

Craftax: A Lightning-Fast Benchmark for Open-Ended Reinforcement Learning
by: Matthews, Michael, et al.
Published: (2024)

Everything Everywhere All at Once: LLMs can In-Context Learn Multiple Tasks in Superposition
by: Xiong, Zheyang, et al.
Published: (2024)

Can Learned Optimization Make Reinforcement Learning Less Difficult?
by: Goldie, Alexander David, et al.
Published: (2024)

A Clean Slate for Offline Reinforcement Learning
by: Jackson, Matthew Thomas, et al.
Published: (2025)

Learning to Reason at the Frontier of Learnability
by: Foster, Thomas, et al.
Published: (2025)

Procedural Generation of Algorithm Discovery Tasks in Machine Learning
by: Goldie, Alexander D., et al.
Published: (2026)

Discovering Temporally-Aware Reinforcement Learning Algorithms
by: Jackson, Matthew Thomas, et al.
Published: (2024)

A Definition of Open-Ended Learning Problems for Goal-Conditioned Agents
by: Sigaud, Olivier, et al.
Published: (2023)

DéjàQ: Open-Ended Evolution of Diverse, Learnable and Verifiable Problems
by: Röpke, Willem, et al.
Published: (2026)

Querying Everything Everywhere All at Once: Supervaluationism for the Agentic Lakehouse
by: Tagliabue, Jacopo
Published: (2026)

A Model-Based Solution to the Offline Multi-Agent Reinforcement Learning Coordination Problem
by: Barde, Paul, et al.
Published: (2023)

HyperVLA: Efficient Inference in Vision-Language-Action Models via Hypernetworks
by: Xiong, Zheng, et al.
Published: (2025)

Null Counterfactual Factor Interactions for Goal-Conditioned Reinforcement Learning
by: Chuck, Caleb, et al.
Published: (2025)

Policy Learning with a Language Bottleneck
by: Srivastava, Megha, et al.
Published: (2024)

CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning
by: Colas, Cédric, et al.
Published: (2018)

SMORE: Score Models for Offline Goal-Conditioned Reinforcement Learning
by: Sikchi, Harshit, et al.
Published: (2023)

Mirror Learning: A Unifying Framework of Policy Optimisation
by: Kuba, Jakub Grudzien, et al.
Published: (2022)

Backward Learning for Goal-Conditioned Policies
by: Höftmann, Marc, et al.
Published: (2023)

The Edge-of-Reach Problem in Offline Model-Based Reinforcement Learning
by: Sims, Anya, et al.
Published: (2024)

Can we Retrieve Everything All at Once? ARM: An Alignment-Oriented LLM-based Retrieval Method
by: Chen, Peter Baile, et al.
Published: (2025)

SOReL and TOReL: Two Methods for Fully Offline Reinforcement Learning
by: Fellows, Mattie, et al.
Published: (2025)

Imagined Autocurricula
by: Güzel, Ahmet H., et al.
Published: (2025)

WorldLLM: Improving LLMs' world modeling using curiosity-driven theory-making
by: Levy, Guillaume, et al.
Published: (2025)

Towards General-Purpose Model-Free Reinforcement Learning
by: Fujimoto, Scott, et al.
Published: (2025)

Goal Exploration via Adaptive Skill Distribution for Goal-Conditioned Reinforcement Learning
by: Wu, Lisheng, et al.
Published: (2024)

Proposing Hierarchical Goal-Conditioned Policy Planning in Multi-Goal Reinforcement Learning
by: Rens, Gavin B.
Published: (2025)

SVL: Goal-Conditioned Reinforcement Learning as Survival Learning
by: Tiofack, Franki Nguimatsia, et al.
Published: (2026)