Cover Image

The Formalism-Implementation Gap in Reinforcement Learning Research

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Castro, Pablo Samuel
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

A Survey of State Representation Learning for Deep Reinforcement Learning
by: Echchahed, Ayoub, et al.
Published: (2025)

Mind the GAP! The Challenges of Scale in Pixel-based Deep Reinforcement Learning
by: Sokar, Ghada, et al.
Published: (2025)

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks
by: Mayor, Walter, et al.
Published: (2025)

Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn
by: Tang, Hongyao, et al.
Published: (2025)

CALE: Continuous Arcade Learning Environment
by: Farebrother, Jesse, et al.
Published: (2024)

Stable Deep Reinforcement Learning via Isotropic Gaussian Representations
by: Pasand, Ali Saheb, et al.
Published: (2026)

Multi-Task Reinforcement Learning Enables Parameter Scaling
by: McLean, Reginald, et al.
Published: (2025)

Studying the Interplay Between the Actor and Critic Representations in Reinforcement Learning
by: Garcin, Samuel, et al.
Published: (2025)

The Generalization Gap in Offline Reinforcement Learning
by: Mediratta, Ishita, et al.
Published: (2023)

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

GAR: Generative Adversarial Reinforcement Learning for Formal Theorem Proving
by: Wang, Ruida, et al.
Published: (2025)

Transformers Provably Implement In-Context Reinforcement Learning with Policy Improvement
by: Liang, Haodong, et al.
Published: (2026)

Bridging Domain Gaps with Target-Aligned Generation for Offline Reinforcement Learning
by: Kim, Minung, et al.
Published: (2026)

Achieving the Tightest Relaxation of Sigmoids for Formal Verification
by: Chevalier, Samuel, et al.
Published: (2024)

Bridging the Performance Gap Between Target-Free and Target-Based Reinforcement Learning
by: Vincent, Théo, et al.
Published: (2025)

Empirical Design in Reinforcement Learning
by: Patterson, Andrew, et al.
Published: (2023)

Probabilistic Constrained Reinforcement Learning with Formal Interpretability
by: Wang, Yanran, et al.
Published: (2023)

The Definitive Guide to Policy Gradients in Deep Reinforcement Learning: Theory, Algorithms and Implementations
by: Lehmann, Matthias
Published: (2024)

Understanding What Affects the Generalization Gap in Visual Reinforcement Learning: Theory and Empirical Evidence
by: Lyu, Jiafei, et al.
Published: (2024)

Bridging Dynamics Gaps via Diffusion Schrödinger Bridge for Cross-Domain Reinforcement Learning
by: Zhang, Hanping, et al.
Published: (2026)

Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback
by: Ying, Wangyang, et al.
Published: (2025)

Bridging the Sim-to-Real Gap in Reinforcement Learning-Based Industrial Dispatching through Execution Semantics
by: Hoss, Jonathan, et al.
Published: (2026)

Formally Verifying Deep Reinforcement Learning Controllers with Lyapunov Barrier Certificates
by: Mandal, Udayan, et al.
Published: (2024)

In value-based deep reinforcement learning, a pruned network is a good network
by: Obando-Ceron, Johan, et al.
Published: (2024)

Learning to Generate Formally Verifiable Step-by-Step Logic Reasoning via Structured Formal Intermediaries
by: Chen, Luoxin, et al.
Published: (2026)

A Research Agenda for Usability and Generalisation in Reinforcement Learning
by: Soemers, Dennis J. N. J., et al.
Published: (2024)

AltNet: Addressing the Plasticity-Stability Dilemma in Reinforcement Learning
by: Maheshwari, Mansi, et al.
Published: (2025)

Plasticine: Accelerating Research in Plasticity-Motivated Deep Reinforcement Learning
by: Yuan, Mingqi, et al.
Published: (2025)

CORE: Concept-Oriented Reinforcement for Bridging the Definition-Application Gap in Mathematical Reasoning
by: Gao, Zijun, et al.
Published: (2025)

Product Interaction: An Algebraic Formalism for Deep Learning Architectures
by: Dong, Haonan, et al.
Published: (2026)

Perception Learning: A Formal Separation of Sensory Representation Learning from Decision Learning
by: Sanyal, Suman
Published: (2025)

Which Rewards Matter? Reward Selection for Reinforcement Learning under Limited Feedback
by: Chaudhari, Shreyas, et al.
Published: (2025)

Superhuman AI for Stratego Using Self-Play Reinforcement Learning and Test-Time Search
by: Sokota, Samuel, et al.
Published: (2025)

Rank-1 Approximation of Inverse Fisher for Natural Policy Gradients in Deep Reinforcement Learning
by: Huo, Yingxiao, et al.
Published: (2026)

Implementing Reinforcement Learning Datacenter Congestion Control in NVIDIA NICs
by: Fuhrer, Benjamin, et al.
Published: (2022)

Reinforcement Learning for Generative AI: State of the Art, Opportunities and Open Research Challenges
by: Franceschelli, Giorgio, et al.
Published: (2023)

Research on Autonomous Robots Navigation based on Reinforcement Learning
by: Wang, Zixiang, et al.
Published: (2024)

DRED: Zero-Shot Transfer in Reinforcement Learning via Data-Regularised Environment Design
by: Garcin, Samuel, et al.
Published: (2024)

FormalMATH: Benchmarking Formal Mathematical Reasoning of Large Language Models
by: Yu, Zhouliang, et al.
Published: (2025)

Quantifying Multimodal Capabilities: Formal Generalization Guarantees in Pairwise Metric Learning
by: Zhou, Richeng, et al.
Published: (2026)