Copertina

Reinforcement Learning with Quasi-Hyperbolic Discounting

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Eshwar, S. R., Motwani, Mayank, Roy, Nibedita, Thoppe, Gugan
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Teaching Precommitted Agents: Model-Free Policy Evaluation and Control in Quasi-Hyperbolic Discounted MDPs
di: Eshwar, S. R.
Pubblicazione: (2025)

Reliable Policy Iteration: Performance Robustness Across Architecture and Environment Perturbations
di: Eshwar, S. R., et al.
Pubblicazione: (2025)

Reinforcement Learning for Exponential Utility: Algorithms and Convergence in Discounted MDPs
di: Thoppe, Gugan, et al.
Pubblicazione: (2026)

Global Convergence Guarantees for Federated Policy Gradient Methods with Adversaries
di: Ganesh, Swetha, et al.
Pubblicazione: (2024)

Policy Gradient with Tree Expansion
di: Dalal, Gal, et al.
Pubblicazione: (2023)

Monotone and Conservative Policy Iteration Beyond the Tabular Case
di: Eshwar, S. R., et al.
Pubblicazione: (2025)

Adversary-Robust Learning from Fully Asynchronous Directional Derivative Estimates
di: Paul, Anik Kumar, et al.
Pubblicazione: (2026)

Does DQN Learn?
di: Gopalan, Aditya, et al.
Pubblicazione: (2022)

Tight Convergence Rates for Online Distributed Linear Estimation with Adversarial Measurements
di: Roy, Nibedita, et al.
Pubblicazione: (2026)

Parameter-free Optimal Rates for Nonlinear Semi-Norm Contractions with Applications to $Q$-Learning
di: Naskar, Ankur, et al.
Pubblicazione: (2025)

Online Learning of Weakly Coupled MDP Policies for Load Balancing and Auto Scaling
di: Eshwar, S. R., et al.
Pubblicazione: (2024)

Partial Identifiability in Inverse Reinforcement Learning For Agents With Non-Exponential Discounting
di: Skalse, Joar, et al.
Pubblicazione: (2024)

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

Understanding and Improving Hyperbolic Deep Reinforcement Learning
di: Klein, Timo, et al.
Pubblicazione: (2025)

Parameter-Free Federated TD Learning with Markov Noise in Heterogeneous Environments
di: Naskar, Ankur, et al.
Pubblicazione: (2025)

Risk Estimation in a Markov Cost Process: Lower and Upper Bounds
di: Thoppe, Gugan, et al.
Pubblicazione: (2023)

h1: Bootstrapping LLMs to Reason over Longer Horizons via Reinforcement Learning
di: Motwani, Sumeet Ramesh, et al.
Pubblicazione: (2025)

Surrogate-Assisted Evolutionary Reinforcement Learning Based on Autoencoder and Hyperbolic Neural Network
di: Li, Bingdong, et al.
Pubblicazione: (2025)

Imitation Learning from Observation with Automatic Discount Scheduling
di: Liu, Yuyang, et al.
Pubblicazione: (2023)

ConvoLearn: A Learning Sciences Grounded Dataset for Fine-Tuning Dialogic AI Tutors
di: Sharma, Mayank, et al.
Pubblicazione: (2026)

CAPSULE: Control-Theoretic Action Perturbations for Safe Uncertainty-Aware Reinforcement Learning
di: Narava, Rahul, et al.
Pubblicazione: (2026)

Action-Inspired Generative Models
di: A., Eshwar R., et al.
Pubblicazione: (2026)

DISCO: An End-to-End Bandit Framework for Personalised Discount Allocation
di: Zhang, Jason Shuo, et al.
Pubblicazione: (2024)

Agent Q: Advanced Reasoning and Learning for Autonomous AI Agents
di: Putta, Pranav, et al.
Pubblicazione: (2024)

ALIGN-FL: Architecture-independent Learning through Invariant Generative component sharing in Federated Learning
di: Gulati, Mayank, et al.
Pubblicazione: (2025)

Sample Efficient Active Algorithms for Offline Reinforcement Learning
di: Roy, Soumyadeep, et al.
Pubblicazione: (2026)

Satisficing Exploration for Deep Reinforcement Learning
di: Arumugam, Dilip, et al.
Pubblicazione: (2024)

Hyperbolic Learning with Multimodal Large Language Models
di: Mandica, Paolo, et al.
Pubblicazione: (2024)

Adaptive Discounting of Training Time Attacks
di: Bector, Ridhima, et al.
Pubblicazione: (2024)

Sequential Attention-based Sampling for Histopathological Analysis
di: G, Tarun, et al.
Pubblicazione: (2025)

Hyperbolic Deep Learning for Foundation Models: A Survey
di: He, Neil, et al.
Pubblicazione: (2025)

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

Reinforcement Learning from Delayed Observations via World Models
di: Karamzade, Armin, et al.
Pubblicazione: (2024)

Throughput Optimization as a Strategic Lever in Large-Scale AI Systems: Evidence from Dataloader and Memory Profiling Innovations
di: Jha, Mayank
Pubblicazione: (2026)

Second-Order Actor-Critic Methods for Discounted MDPs via Policy Hessian Decomposition
di: Manivannan, Sanjeev, et al.
Pubblicazione: (2026)

Enhance Hyperbolic Representation Learning via Second-order Pooling
di: Song, Kun, et al.
Pubblicazione: (2024)

Understanding and Mitigating Hyperbolic Dimensional Collapse in Graph Contrastive Learning
di: Zhang, Yifei, et al.
Pubblicazione: (2023)

Reinforcement Learning with Pairwise Preferences in Long-Term Decision Problems
di: Carr, Jonathan Colaço, et al.
Pubblicazione: (2026)

Model-Based Reinforcement Learning under Random Observation Delays
di: Karamzade, Armin, et al.
Pubblicazione: (2025)

Hyperbolic Random Forests
di: Doorenbos, Lars, et al.
Pubblicazione: (2023)