Copertina

Action Gaps and Advantages in Continuous-Time Distributional Reinforcement Learning

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Wiltzer, Harley, Bellemare, Marc G., Meger, David, Shafto, Patrick, Jhaveri, Yash
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning Optimization and Control
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Convergence Theorems for Entropy-Regularized and Distributional Reinforcement Learning
di: Jhaveri, Yash, et al.
Pubblicazione: (2025)

Tractable Representations for Convergent Approximation of Distributional HJB Equations
di: Alhosh, Julie, et al.
Pubblicazione: (2025)

Foundations of Multivariate Distributional Reinforcement Learning
di: Wiltzer, Harley, et al.
Pubblicazione: (2024)

Operator Models for Continuous-Time Offline Reinforcement Learning
di: Hoischen, Nicolas, et al.
Pubblicazione: (2025)

Policy Optimization in Hybrid Discrete-Continuous Action Spaces via Mixed Gradients
di: Alvo, Matias, et al.
Pubblicazione: (2026)

Continuous Q-Score Matching: Diffusion Guided Reinforcement Learning for Continuous-Time Control
di: Hua, Chengxiu, et al.
Pubblicazione: (2025)

Score as Action: Fine-Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning
di: Zhao, Hanyang, et al.
Pubblicazione: (2025)

Policy Transfer for Continuous-Time Reinforcement Learning: A (Rough) Differential Equation Approach
di: Guo, Xin, et al.
Pubblicazione: (2025)

Federated Learning Can Find Friends That Are Advantageous
di: Tupitsa, Nazarii, et al.
Pubblicazione: (2024)

On the Foundation of Distributionally Robust Reinforcement Learning
di: Wang, Shengbo, et al.
Pubblicazione: (2023)

Tail Distribution of Regret in Optimistic Reinforcement Learning
di: Khodadadian, Sajad, et al.
Pubblicazione: (2025)

Integrating Human Knowledge Through Action Masking in Reinforcement Learning for Operations Research
di: Stappert, Mirko, et al.
Pubblicazione: (2025)

Deterministic Policy Gradient for Reinforcement Learning with Continuous Time and State
di: Cheng, Ziheng, et al.
Pubblicazione: (2025)

Policy Optimization in a Noisy Neighborhood: On Return Landscapes in Continuous Control
di: Rahn, Nate, et al.
Pubblicazione: (2023)

Sample Complexity of Distributionally Robust Average-Reward Reinforcement Learning
di: Chen, Zijun, et al.
Pubblicazione: (2025)

Deep Reinforcement Learning for Infinite Horizon Mean Field Problems in Continuous Spaces
di: Angiuli, Andrea, et al.
Pubblicazione: (2023)

Sublinear Regret for a Class of Continuous-Time Linear-Quadratic Reinforcement Learning Problems
di: Huang, Yilie, et al.
Pubblicazione: (2024)

Quotient-Categorical Representations for Bellman-Compatible Average-Reward Distributional Reinforcement Learning
di: Kaya, Ege C., et al.
Pubblicazione: (2026)

Finite-Time Analysis of On-Policy Heterogeneous Federated Reinforcement Learning
di: Zhang, Chenyu, et al.
Pubblicazione: (2024)

Reinforcement Learning with Random Time Horizons
di: Borrell, Enric Ribera, et al.
Pubblicazione: (2025)

Solving the Paint Shop Problem with Flexible Management of Multi-Lane Buffers Using Reinforcement Learning and Action Masking
di: Stappert, Mirko, et al.
Pubblicazione: (2025)

Action Dependency Graphs for Globally Optimal Coordinated Reinforcement Learning
di: Ding, Jianglin, et al.
Pubblicazione: (2025)

Fast Two-Time-Scale Stochastic Gradient Method with Applications in Reinforcement Learning
di: Zeng, Sihan, et al.
Pubblicazione: (2024)

Safe Reinforcement Learning for Constrained Markov Decision Processes with Stochastic Stopping Time
di: Mazumdar, Abhijit, et al.
Pubblicazione: (2024)

Reinforcement Learning for a Discrete-Time Linear-Quadratic Control Problem with an Application
di: Li, Lucky
Pubblicazione: (2024)

Risk-Sensitive Q-Learning in Continuous Time with Application to Dynamic Portfolio Selection
di: Xie, Chuhan
Pubblicazione: (2025)

Data-Driven Exploration for a Class of Continuous-Time Indefinite Linear--Quadratic Reinforcement Learning Problems
di: Huang, Yilie, et al.
Pubblicazione: (2025)

A Two-Time-Scale Stochastic Optimization Framework with Applications in Control and Reinforcement Learning
di: Zeng, Sihan, et al.
Pubblicazione: (2021)

Continuous-Time Reinforcement Learning for Asset-Liability Management
di: Huang, Yilie
Pubblicazione: (2025)

Multi-Timescale Primal Dual Hybrid Gradient with Application to Distributed Optimization
di: Zhang, Junhui, et al.
Pubblicazione: (2025)

Distributed Learning over Arbitrary Topology: Linear Speed-Up with Polynomial Transient Time
di: You, Runze, et al.
Pubblicazione: (2025)

Mitigating Forgetting in Continual Learning with Selective Gradient Projection
di: Singh, Anika, et al.
Pubblicazione: (2026)

Decoupled Continuous-Time Reinforcement Learning via Hamiltonian Flow
di: Nguyen, Minh
Pubblicazione: (2026)

Attentive Convolutional Deep Reinforcement Learning for Optimizing Solar-Storage Systems in Real-Time Electricity Markets
di: Li, Jinhao, et al.
Pubblicazione: (2024)

Distributionally Robust Optimization via Iterative Algorithms in Continuous Probability Spaces
di: Zhu, Linglingzhi, et al.
Pubblicazione: (2024)

Towards Understanding Generalization and Stability Gaps between Centralized and Decentralized Federated Learning
di: Sun, Yan, et al.
Pubblicazione: (2023)

Resilient Constrained Reinforcement Learning
di: Ding, Dongsheng, et al.
Pubblicazione: (2023)

Generalized Continuous-Time Models for Nesterov's Accelerated Gradient Methods
di: Park, Chanwoong, et al.
Pubblicazione: (2024)

TreeDQN: Sample-Efficient Off-Policy Reinforcement Learning for Combinatorial Optimization
di: Sorokin, D., et al.
Pubblicazione: (2023)

A Pontryagin Perspective on Reinforcement Learning
di: Eberhard, Onno, et al.
Pubblicazione: (2024)