Add to ORKGThis paper is about representation in RL.We discuss some of the concepts in representation and generalization in reinforcement learning and argue for higher-order representations, instead of the commonly used propositional representations. The paper contains a small review of current reinforcement learning systems using higher-order representations, followed by a brief discussion. The paper ends with research directions and open problems.\u
To be successful in real-world tasks, Reinforcement Learning (RL) needs to exploit the compositional...
In this work, we introduce the first approach to the Inverse Reinforcement Learning (IRL) problem in...
Abstract. In recent years, there has been a growing interest in using rich repre-sentations such as ...
Reinforcement learning (RL) has developed into a primary approach to learning control strate-gies fo...
Reinforcement learning has developed into a primary approach for learning control strategies for aut...
Reinforcement learning has developed into a primary approach for learning control strategies for aut...
© Springer-Verlag Berlin Heidelberg 1998. Relational reinforcement learning is presented, a learning...
We present a perspective and challenges for Relational Reinforcement Learning (RRL). We first survey...
Reinforcement learning (RL) has developed into a primary approach to learning control strategies for...
In recent years, there has been a growing interest in using rich representations such as relational...
Abstract. In reinforcement learning, an agent tries to learn a policy, i.e., how to select an action...
Relational Reinforcement Learning (RRL) is both a young and an old field. In this paper, we trace th...
Humans perceive the world in terms of objects and relations between them. In fact, for any given pai...
Relational reinforcement learning is a promising direction within reinforcement learning research. I...
In this paper we present a new method for reinforcement learning in relational domains. A logical la...
To be successful in real-world tasks, Reinforcement Learning (RL) needs to exploit the compositional...
In this work, we introduce the first approach to the Inverse Reinforcement Learning (IRL) problem in...
Abstract. In recent years, there has been a growing interest in using rich repre-sentations such as ...
Reinforcement learning (RL) has developed into a primary approach to learning control strate-gies fo...
Reinforcement learning has developed into a primary approach for learning control strategies for aut...
Reinforcement learning has developed into a primary approach for learning control strategies for aut...
© Springer-Verlag Berlin Heidelberg 1998. Relational reinforcement learning is presented, a learning...
We present a perspective and challenges for Relational Reinforcement Learning (RRL). We first survey...
Reinforcement learning (RL) has developed into a primary approach to learning control strategies for...
In recent years, there has been a growing interest in using rich representations such as relational...
Abstract. In reinforcement learning, an agent tries to learn a policy, i.e., how to select an action...
Relational Reinforcement Learning (RRL) is both a young and an old field. In this paper, we trace th...
Humans perceive the world in terms of objects and relations between them. In fact, for any given pai...
Relational reinforcement learning is a promising direction within reinforcement learning research. I...
In this paper we present a new method for reinforcement learning in relational domains. A logical la...
To be successful in real-world tasks, Reinforcement Learning (RL) needs to exploit the compositional...
In this work, we introduce the first approach to the Inverse Reinforcement Learning (IRL) problem in...
Abstract. In recent years, there has been a growing interest in using rich repre-sentations such as ...