Cover Image

An Empirical Investigation of Value-Based Multi-objective Reinforcement Learning for Stochastic Environments

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Ding, Kewen, Vamplew, Peter, Foale, Cameron, Dazeley, Richard
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Issues with Value-Based Multi-objective Reinforcement Learning: Value Function Interference and Overestimation Sensitivity
by: Vamplew, Peter, et al.
Published: (2024)

Multi-objective Reinforcement Learning With Augmented States Requires Rewards After Deployment
by: Vamplew, Peter, et al.
Published: (2026)

Multi-objective Reinforcement Learning: A Tool for Pluralistic Alignment
by: Vamplew, Peter, et al.
Published: (2024)

ES-C51: Expected Sarsa Based C51 Distributional Reinforcement Learning Algorithm
by: Tandon, Rijul, et al.
Published: (2025)

Utility-Based Reinforcement Learning: Unifying Single-objective and Multi-objective Reinforcement Learning
by: Vamplew, Peter, et al.
Published: (2024)

Learning the Value Systems of Societies with Preference-based Multi-objective Reinforcement Learning
by: Holgado-Sánchez, Andrés, et al.
Published: (2026)

Adaptive Alignment: Dynamic Preference Adjustments via Multi-Objective Reinforcement Learning for Pluralistic AI
by: Harland, Hadassah, et al.
Published: (2024)

On Generalization Across Environments In Multi-Objective Reinforcement Learning
by: Teoh, Jayden, et al.
Published: (2025)

Ensemble Elastic DQN: A novel multi-step ensemble approach to address overestimation in deep value-based reinforcement learning
by: Ly, Adrian, et al.
Published: (2025)

Learning Rewards, Not Labels: Adversarial Inverse Reinforcement Learning for Machinery Fault Detection
by: Neupane, Dhiraj, et al.
Published: (2026)

Multi-Agent Reinforcement Learning-Based UAV Pathfinding for Obstacle Avoidance in Stochastic Environment
by: Wu, Qizhen, et al.
Published: (2023)

Factored Value Functions for Graph-Based Multi-Agent Reinforcement Learning
by: Rashwan, Ahmed, et al.
Published: (2026)

Value-Based Deep Multi-Agent Reinforcement Learning with Dynamic Sparse Training
by: Hu, Pihe, et al.
Published: (2024)

End-to-end Deep Reinforcement Learning for Stochastic Multi-objective Optimization in C-VRPTW
by: Abouelrous, Abdo, et al.
Published: (2025)

Data-driven Machinery Fault Diagnosis: A Comprehensive Review
by: Neupane, Dhiraj, et al.
Published: (2024)

Extreme Value Policy Optimization for Safe Reinforcement Learning
by: Gao, Shiqing, et al.
Published: (2026)

Control in Stochastic Environment with Delays: A Model-based Reinforcement Learning Approach
by: Yao, Zhiyuan, et al.
Published: (2024)

Value-Distributional Model-Based Reinforcement Learning
by: Luis, Carlos E., et al.
Published: (2023)

Multi-Phase Spacecraft Trajectory Optimization via Transformer-Based Reinforcement Learning
by: Jain, Amit, et al.
Published: (2025)

Wireless Communication Enhanced Value Decomposition for Multi-Agent Reinforcement Learning
by: Hu, Diyi, et al.
Published: (2026)

Value-Guidance MeanFlow for Offline Multi-Agent Reinforcement Learning
by: Pang, Teng, et al.
Published: (2026)

Fixing Incomplete Value Function Decomposition for Multi-Agent Reinforcement Learning
by: Baisero, Andrea, et al.
Published: (2025)

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

BoreaRL: A Multi-Objective Reinforcement Learning Environment for Climate-Adaptive Boreal Forest Management
by: Dsouza, Kevin Bradley, et al.
Published: (2025)

Is there Value in Reinforcement Learning?
by: Fox, Lior, et al.
Published: (2025)

Don't Forget the Critic: Value-Based Data Rehearsal for Multi-Cyclic Continual Reinforcement Learning
by: Poole, Benjamin, et al.
Published: (2026)

Continuous-Time Value Iteration for Multi-Agent Reinforcement Learning
by: Wang, Xuefeng, et al.
Published: (2025)

The Value of Reward Lookahead in Reinforcement Learning
by: Merlis, Nadav, et al.
Published: (2024)

A Tensor Low-Rank Approximation for Value Functions in Multi-Task Reinforcement Learning
by: Rozada, Sergio, et al.
Published: (2025)

Discovering Minimal Reinforcement Learning Environments
by: Liesen, Jarek, et al.
Published: (2024)

Synthetic Monitoring Environments for Reinforcement Learning
by: Pleiss, Leonard, et al.
Published: (2026)

Federated Reinforcement Learning in Heterogeneous Environments
by: Hwang, Ukjo, et al.
Published: (2025)

Shapley Value Based Multi-Agent Reinforcement Learning: Theory, Method and Its Application to Energy Network
by: Wang, Jianhong
Published: (2024)

Distributed Multi-Agent Reinforcement Learning Based on Graph-Induced Local Value Functions
by: Jing, Gangshan, et al.
Published: (2022)

Combating Toxic Language: A Review of LLM-Based Strategies for Software Engineering
by: Zhuo, Hao, et al.
Published: (2025)

Stochastic Decision Horizons for Constrained Reinforcement Learning
by: Milosevic, Nikola, et al.
Published: (2026)

Ensemble Value Functions for Efficient Exploration in Multi-Agent Reinforcement Learning
by: Schäfer, Lukas, et al.
Published: (2023)

Walking the Values in Bayesian Inverse Reinforcement Learning
by: Bajgar, Ondrej, et al.
Published: (2024)

Empirical Bayesian Multi-Bandit Learning
by: Jiang, Xia, et al.
Published: (2025)

BXRL: Behavior-Explainable Reinforcement Learning
by: Rachum, Ram, et al.
Published: (2026)