Cover Image

Burning RED: Unlocking Subtask-Driven Reinforcement Learning and Risk-Awareness in Average-Reward Markov Decision Processes

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Rojas, Juan Sebastian, Lee, Chi-Guhn
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Ergodic Risk Measures: Towards a Risk-Aware Foundation for Continual Reinforcement Learning
by: Rojas, Juan Sebastian, et al.
Published: (2025)

A Differential Perspective on Distributional Reinforcement Learning
by: Rojas, Juan Sebastian, et al.
Published: (2025)

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

Hierarchical Average-Reward Linearly-solvable Markov Decision Processes
by: Infante, Guillermo, et al.
Published: (2024)

Almost Sure Convergence of Differential Temporal Difference Learning for Average Reward Markov Decision Processes
by: Blaser, Ethan, et al.
Published: (2026)

Learning Bilateral Team Formation in Cooperative Multi-Agent Reinforcement Learning
by: Moslemi, Koorosh, et al.
Published: (2025)

Subtask-Aware Visual Reward Learning from Segmented Demonstrations
by: Kim, Changyeon, et al.
Published: (2025)

Provably Efficient Reward Transfer in Reinforcement Learning with Discrete Markov Decision Processes
by: Vora, Kevin, et al.
Published: (2025)

Diffusion-Augmented Markov Decision Processes for Maximum Entropy Reinforcement Learning
by: Sanokowski, Sebastian, et al.
Published: (2025)

Regret Analysis of Policy Gradient Algorithm for Infinite Horizon Average Reward Markov Decision Processes
by: Bai, Qinbo, et al.
Published: (2023)

Quantum Speedups in Regret Analysis of Infinite Horizon Average-Reward Markov Decision Processes
by: Ganguly, Bhargav, et al.
Published: (2023)

Globally Optimal Hierarchical Reinforcement Learning for Linearly-Solvable Markov Decision Processes
by: Infante, Guillermo, et al.
Published: (2021)

Provably Adaptive Average Reward Reinforcement Learning for Metric Spaces
by: Kar, Avik, et al.
Published: (2024)

Reinforcement Learning in Switching Non-Stationary Markov Decision Processes: Algorithms and Convergence Analysis
by: Amiri, Mohsen, et al.
Published: (2025)

Model-Based Reinforcement Learning in Discrete-Action Non-Markovian Reward Decision Processes
by: Trapasso, Alessandro, et al.
Published: (2025)

A Harmonic Mean Formulation of Average Reward Reinforcement Learning in SMDPs
by: Shtossel, Erel, et al.
Published: (2026)

Efficient $Q$-Learning and Actor-Critic Methods for Robust Average Reward Reinforcement Learning
by: Xu, Yang, et al.
Published: (2025)

Optimal Decision Tree Policies for Markov Decision Processes
by: Vos, Daniël, et al.
Published: (2023)

Decision-Focused Model-based Reinforcement Learning for Reward Transfer
by: Sharma, Abhishek, et al.
Published: (2023)

Kernel-Based Function Approximation for Average Reward Reinforcement Learning: An Optimist No-Regret Algorithm
by: Vakili, Sattar, et al.
Published: (2024)

Constrained Reinforcement Learning with Average Reward Objective: Model-Based and Model-Free Algorithms
by: Aggarwal, Vaneet, et al.
Published: (2024)

SPARK: Stepwise Process-Aware Rewards for Reference-Free Reinforcement Learning
by: Rahman, Salman, et al.
Published: (2025)

Act as You Learn: Adaptive Decision-Making in Non-Stationary Markov Decision Processes
by: Luo, Baiting, et al.
Published: (2024)

Markov Decision Processes under External Temporal Processes
by: Ayyagari, Ranga Shaarad, et al.
Published: (2023)

Inferring Reward Machines and Transition Machines from Partially Observable Markov Decision Processes
by: Wu, Yuly, et al.
Published: (2025)

Reinforcement Learning with LTL and $ω$-Regular Objectives via Optimality-Preserving Translation to Average Rewards
by: Le, Xuan-Bach, et al.
Published: (2024)

OCMDP: Observation-Constrained Markov Decision Process
by: Wang, Taiyi, et al.
Published: (2024)

Optimistic Regret Bounds for Online Learning in Adversarial Markov Decision Processes
by: Moon, Sang Bin, et al.
Published: (2024)

Policy Gradient for Robust Markov Decision Processes
by: Wang, Qiuhao, et al.
Published: (2024)

Thermodynamics of Reinforcement Learning Curricula
by: Adamczyk, Jacob, et al.
Published: (2026)

Identifying Selections for Unsupervised Subtask Discovery
by: Qiu, Yiwen, et al.
Published: (2024)

An Offline Risk-aware Policy Selection Method for Bayesian Markov Decision Processes
by: Angelotti, Giorgio, et al.
Published: (2021)

Sample-Efficient Preference-based Reinforcement Learning with Dynamics Aware Rewards
by: Metcalf, Katherine, et al.
Published: (2024)

Improved Sample Complexity Analysis of Natural Policy Gradient Algorithm with General Parameterization for Infinite Horizon Discounted Reward Markov Decision Processes
by: Mondal, Washim Uddin, et al.
Published: (2023)

WARC-Bench: Web Archive Based Benchmark for GUI Subtask Executions
by: Srivastava, Sanjari, et al.
Published: (2025)

A Cantor-Kantorovich Metric Between Markov Decision Processes with Application to Transfer Learning
by: Banse, Adrien, et al.
Published: (2024)

Policy Gradient Algorithms with Monte Carlo Tree Learning for Non-Markov Decision Processes
by: Morimura, Tetsuro, et al.
Published: (2022)

An Innovative Data-Driven and Adaptive Reinforcement Learning Approach for Context-Aware Prescriptive Process Monitoring
by: Abbasi, Mostafa, et al.
Published: (2025)

Solving Robust Markov Decision Processes: Generic, Reliable, Efficient
by: Meggendorfer, Tobias, et al.
Published: (2024)

SPOT: Scalable Policy Optimization with Trees for Markov Decision Processes
by: Xiong, Xuyuan, et al.
Published: (2025)