:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Weaver, Lex, Baxter, Jonathan
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2512.08855
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

A Multi-Agent, Policy-Gradient approach to Network Routing
by: Tao, Nigel, et al.
Published: (2025)

Reinforcement Learning in POMDP's via Direct Gradient Ascent
by: Baxter, Jonathan, et al.
Published: (2025)

Scaling Internal-State Policy-Gradient Methods for POMDPs
by: Aberdeen, Douglas, et al.
Published: (2025)

The Evolution of Learning Algorithms for Artificial Neural Networks
by: Baxter, Jonathan
Published: (2025)

A result relating convex n-widths to covering numbers with some applications to neural networks
by: Baxter, Jonathan, et al.
Published: (2025)

Transformers Can Learn Temporal Difference Methods for In-Context Reinforcement Learning
by: Wang, Jiuqi, et al.
Published: (2024)

Optimal Transport-Guided Safety in Temporal Difference Reinforcement Learning
by: Shahrooei, Zahra, et al.
Published: (2025)

Deep Reinforcement Learning and The Tale of Two Temporal Difference Errors
by: Rojas, Juan Sebastian, et al.
Published: (2026)

Generalized Gaussian Temporal Difference Error for Uncertainty-aware Reinforcement Learning
by: Kim, Seyeon, et al.
Published: (2024)

Differentiable Fuzzy Neural Networks for Recommender Systems
by: Bartl, Stephan, et al.
Published: (2025)

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

Simplifying Deep Temporal Difference Learning
by: Gallici, Matteo, et al.
Published: (2024)

An Analysis of Quantile Temporal-Difference Learning
by: Rowland, Mark, et al.
Published: (2023)

On the Statistical Benefits of Temporal Difference Learning
by: Cheikhi, David, et al.
Published: (2023)

Meta-Learning and Targeted Differential Privacy to Improve the Accuracy-Privacy Trade-off in Recommendations
by: Müllner, Peter, et al.
Published: (2026)

A New Error Temporal Difference Algorithm for Deep Reinforcement Learning in Microgrid Optimization
by: Yao, Fulong, et al.
Published: (2025)

Discerning Temporal Difference Learning
by: Ma, Jianfei
Published: (2023)

Backstepping Temporal Difference Learning
by: Lim, Han-Dong, et al.
Published: (2023)

AdaGamma: State-Dependent Discounting for Temporal Adaptation in Reinforcement Learning
by: Wang, Yaomin, et al.
Published: (2026)

Temporal Difference Learning with Compressed Updates: Error-Feedback meets Reinforcement Learning
by: Mitra, Aritra, et al.
Published: (2023)

Towards Parameter-Free Temporal Difference Learning
by: Li, Yunxiang, et al.
Published: (2026)

Temporal Difference Learning with Constrained Initial Representations
by: Lyu, Jiafei, et al.
Published: (2026)

New Versions of Gradient Temporal Difference Learning
by: Lee, Donghwan, et al.
Published: (2021)

Temporal-Difference Variational Continual Learning
by: Melo, Luckeciano C., et al.
Published: (2024)

Gradient Iterated Temporal-Difference Learning
by: Vincent, Théo, et al.
Published: (2026)

Implicit Updates for Average-Reward Temporal Difference Learning
by: Kim, Hwanwoo, et al.
Published: (2025)

n-Step Temporal Difference Learning with Optimal n
by: Mandal, Lakshmi, et al.
Published: (2023)

Temporal Abstraction in Reinforcement Learning with Offline Data
by: Ayyagari, Ranga Shaarad, et al.
Published: (2024)

Advantage-based Temporal Attack in Reinforcement Learning
by: He, Shenghong
Published: (2026)

Lane Change Intention Prediction of two distinct Populations using a Transformer
by: De Cristofaro, Francesco, et al.
Published: (2025)

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

Accelerated Distributional Temporal Difference Learning with Linear Function Approximation
by: Jin, Kaicheng, et al.
Published: (2025)

Revisiting a Design Choice in Gradient Temporal Difference Learning
by: Qian, Xiaochi, et al.
Published: (2023)

Statistical Inference for Temporal Difference Learning with Linear Function Approximation
by: Wu, Weichen, et al.
Published: (2024)

On the Divergence of Differential Temporal Difference Learning without Local Clocks
by: Antrobius, David, et al.
Published: (2026)

Skill-Critic: Refining Learned Skills for Hierarchical Reinforcement Learning
by: Hao, Ce, et al.
Published: (2023)

From Pixels to Factors: Learning Independently Controllable State Variables for Reinforcement Learning
by: Rodriguez-Sanchez, Rafael, et al.
Published: (2025)

TEACH: Temporal Variance-Driven Curriculum for Reinforcement Learning
by: Chaudhary, Gaurav, et al.
Published: (2025)

TDRM: Smooth Reward Models with Temporal Difference for LLM RL and Inference
by: Zhang, Dan, et al.
Published: (2025)

Is Temporal Difference Learning the Gold Standard for Stitching in RL?
by: Bortkiewicz, Michał, et al.
Published: (2025)