Add to ORKGIn this talk we present PQ-learning, a new Reinforcement Learning (RL) algorithm that determines the rational behaviours of an agent in multi-objective domainsThis work is partially funded by: grant TIN2009-14179 (Spanish Government, Plan Nacional de I+D+i) and Universidad de Málaga, Campus de Excelencia Internacional Andalucía Tech. Manuela Ruiz-Montiel is funded by the Spanish Ministry of Education through the National F.P.U. Progra
This thesis describes reinforcement learning (RL) methods which can solve sequential decision makin...
The paper explores a very simple agent design method called Q-decomposition, wherein a com-plex agen...
In this work we introduce reinforcement learning techniques for solving lexicographic multi-objectiv...
Many real-world problems involve the optimization of multiple, possibly conflicting ob-jectives. Mul...
This work describes MPQ-learning, an temporal-difference method that approximates the set of all non...
The solution for a Multi-Objetive Reinforcement Learning problem is a set of Pareto optimal policie...
This paper describes a novel multi-objective reinforcement learning algorithm. The proposed algorith...
We study fair multi-objective reinforcement learning in which an agent must learn a policy that simu...
Reinforcement Learning (RL) is a powerful and well-studied Machine Learning paradigm, where an agent...
Graduation date: 2005Reinforcement learning (RL) is the study of systems that learn from interaction...
\u3cp\u3eThis paper describes a novel multi-objective reinforcement learning algorithm. The proposed...
Multiobjective reinforcement learning (MORL) extends RL to problems with multiple conflicting object...
The real world is full of problems with multiple conflicting objectives. However, Reinforcement Lear...
Multi-Agent Reinforcement Learning (MARL) is a powerful Machine Learning paradigm, where multiple au...
Reinforcement Learning (RL) implementations achieved great results in recent years, but the majority...
This thesis describes reinforcement learning (RL) methods which can solve sequential decision makin...
The paper explores a very simple agent design method called Q-decomposition, wherein a com-plex agen...
In this work we introduce reinforcement learning techniques for solving lexicographic multi-objectiv...
Many real-world problems involve the optimization of multiple, possibly conflicting ob-jectives. Mul...
This work describes MPQ-learning, an temporal-difference method that approximates the set of all non...
The solution for a Multi-Objetive Reinforcement Learning problem is a set of Pareto optimal policie...
This paper describes a novel multi-objective reinforcement learning algorithm. The proposed algorith...
We study fair multi-objective reinforcement learning in which an agent must learn a policy that simu...
Reinforcement Learning (RL) is a powerful and well-studied Machine Learning paradigm, where an agent...
Graduation date: 2005Reinforcement learning (RL) is the study of systems that learn from interaction...
\u3cp\u3eThis paper describes a novel multi-objective reinforcement learning algorithm. The proposed...
Multiobjective reinforcement learning (MORL) extends RL to problems with multiple conflicting object...
The real world is full of problems with multiple conflicting objectives. However, Reinforcement Lear...
Multi-Agent Reinforcement Learning (MARL) is a powerful Machine Learning paradigm, where multiple au...
Reinforcement Learning (RL) implementations achieved great results in recent years, but the majority...
This thesis describes reinforcement learning (RL) methods which can solve sequential decision makin...
The paper explores a very simple agent design method called Q-decomposition, wherein a com-plex agen...
In this work we introduce reinforcement learning techniques for solving lexicographic multi-objectiv...