Imagen de Portada

The Cell Must Go On: Agar.io for Continual Reinforcement Learning

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Mohamed, Mohamed A., Nekhomiazh, Kateryna, Vyas, Vedant, Jose, Marcos M., Patterson, Andrew, Machado, Marlos C.
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning Artificial Intelligence
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Harnessing Discrete Representations For Continual Reinforcement Learning
por: Meyer, Edan, et al.
Publicado: (2023)

Deep Reinforcement Learning with Gradient Eligibility Traces
por: Elelimy, Esraa, et al.
Publicado: (2025)

AGaLiTe: Approximate Gated Linear Transformers for Online Reinforcement Learning
por: Pramanik, Subhojeet, et al.
Publicado: (2023)

Proper Laplacian Representation Learning
por: Gomez, Diego, et al.
Publicado: (2023)

The Laplacian Keyboard: Beyond the Linear Span
por: Chandrasekar, Siddarth, et al.
Publicado: (2026)

Demystifying the Recency Heuristic in Temporal-Difference Learning
por: Daley, Brett, et al.
Publicado: (2024)

Artificial Generals Intelligence: Mastering Generals.io with Reinforcement Learning
por: Straka, Matej, et al.
Publicado: (2025)

An Analysis of Action-Value Temporal-Difference Methods That Learn State Values
por: Daley, Brett, et al.
Publicado: (2025)

Deep Double Q-learning
por: Nagarajan, Prabhat, et al.
Publicado: (2025)

Weight Clipping for Deep Continual and Reinforcement Learning
por: Elsayed, Mohamed, et al.
Publicado: (2024)

Empirical Design in Reinforcement Learning
por: Patterson, Andrew, et al.
Publicado: (2023)

A Generalized Projected Bellman Error for Off-policy Value Estimation in Reinforcement Learning
por: Patterson, Andrew, et al.
Publicado: (2021)

When is Offline Policy Selection Sample Efficient for Reinforcement Learning?
por: Liu, Vincent, et al.
Publicado: (2023)

EdgeMLOps: Operationalizing ML models with Cumulocity IoT and thin-edge.io for Visual quality Inspection
por: Chaturvedi, Kanishk, et al.
Publicado: (2025)

Addressing Loss of Plasticity and Catastrophic Forgetting in Continual Learning
por: Elsayed, Mohamed, et al.
Publicado: (2024)

Streaming Deep Reinforcement Learning Finally Works
por: Elsayed, Mohamed, et al.
Publicado: (2024)

Trustworthy AI Must Account for Interactions
por: Cresswell, Jesse C.
Publicado: (2025)

Normality-Guided Distributional Reinforcement Learning for Continuous Control
por: Byun, Ju-Seung, et al.
Publicado: (2022)

Deep Learning with Tabular Data: A Self-supervised Approach
por: Vyas, Tirth Kiranbhai
Publicado: (2024)

Explore-Go: Leveraging Exploration for Generalisation in Deep Reinforcement Learning
por: Weltevrede, Max, et al.
Publicado: (2024)

Mitigating Deep Reinforcement Learning Backdoors in the Neural Activation Space
por: Vyas, Sanyam, et al.
Publicado: (2024)

Surprise-Adaptive Intrinsic Motivation for Unsupervised Reinforcement Learning
por: Hugessen, Adriana, et al.
Publicado: (2024)

VARS-FL: Validation-Aligned Client Selection for Non-IID Federated Learning in IoT Systems
por: Lakas, Mohamed, et al.
Publicado: (2026)

Learning Rewards, Not Labels: Adversarial Inverse Reinforcement Learning for Machinery Fault Detection
por: Neupane, Dhiraj, et al.
Publicado: (2026)

Revisiting Scalable Hessian Diagonal Approximations for Applications in Reinforcement Learning
por: Elsayed, Mohamed, et al.
Publicado: (2024)

Intentional Updates for Streaming Reinforcement Learning
por: Sharifnassab, Arsalan, et al.
Publicado: (2026)

Causal-Symbolic Meta-Learning (CSML): Inducing Causal World Models for Few-Shot Generalization
por: S, Mohamed Zayaan
Publicado: (2025)

TADPO: Reinforcement Learning Goes Off-road
por: Wu, Zhouchonghao, et al.
Publicado: (2026)

Position: A Theory of Deep Learning Must Include Compositional Sparsity
por: Danhofer, David A., et al.
Publicado: (2025)

Curriculum Learning for LLM Pretraining: An Analysis of Learning Dynamics
por: Elgaar, Mohamed, et al.
Publicado: (2026)

Towards Learning Foundation Models for Heuristic Functions to Solve Pathfinding Problems
por: Khandelwal, Vedant, et al.
Publicado: (2024)

From Alignment to Prediction: A Study of Self-Supervised Learning and Predictive Representation Learning
por: Dutta, Mintu, et al.
Publicado: (2026)

Simulating the Unseen: Crash Prediction Must Learn from What Did Not Happen
por: Li, Zihao, et al.
Publicado: (2025)

Knowledge Distillation Must Account for What It Loses
por: Wang, Wenshuo
Publicado: (2026)

Recurrent Deep Reinforcement Learning for Chemotherapy Control under Partial Observability
por: Kiram, Firas Mohamed Elamine, et al.
Publicado: (2026)

Residual Reward Models for Preference-based Reinforcement Learning
por: Cao, Chenyang, et al.
Publicado: (2025)

Where You Go is Who You Are: Behavioral Theory-Guided LLMs for Inverse Reinforcement Learning
por: Sun, Yuran, et al.
Publicado: (2025)

Continual Learning as Computationally Constrained Reinforcement Learning
por: Kumar, Saurabh, et al.
Publicado: (2023)

Aligning Brain Signals with Multimodal Speech and Vision Embeddings
por: Shapovalenko, Kateryna, et al.
Publicado: (2025)

ARM-FM: Automated Reward Machines via Foundation Models for Compositional Reinforcement Learning
por: Castanyer, Roger Creus, et al.
Publicado: (2025)