Abstract. Reinforcement learning, and Q-learning in particular, encounter two major problems when dealing with large state spaces. First, learning the Q-function in tabular form may be infeasible because of the excessive amount of memory needed to store the table, and because the Q-function only converges after each state has been visited multiple times. Second, rewards in the state space may be so sparse that with random exploration they will only be discovered extremely slowly. The first problem is often solved by learning a generalization of the encountered examples (e.g., using a neural net or decision tree). Relational reinforcement learning (RRL) is such an approach; it makes Q-learning feasible in structural domains by incorporating ...
This article describes a learning classifier system (LCS) approach to relational reinforcement learn...
In recent years, there has been a growing interest in using rich representations such as relational ...
A fundamental problem in reinforcement learning is balancing exploration and exploitation. We addres...
Abstract. Reinforcement learning, and Q-learning in particular, encounter two major problems when de...
Relational reinforcement learning is presented, a learning technique that combines reinforcement lea...
Relational reinforcement learning (RRL) is a Q-learning technique which uses first or-der regression...
Relational Reinforcement Learning (RRL) is a subfield of machine learning in which a learning agent ...
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...
In recent years, there has been a growing interest in using rich representations such as relational...
We present a perspective and challenges for Relational Reinforcement Learning (RRL). We first survey...
Abstract. In reinforcement learning, an agent tries to learn a policy, i.e., how to select an action...
We propose a novel method for reinforcement learning in domains that are best described using relati...
International audienceIn this work, we introduce the first approach to the Inverse Reinforcement Lea...
RRL is a relational reinforcement learning system based on Q-learning in relational state-action spa...
This article describes a learning classifier system (LCS) approach to relational reinforcement learn...
In recent years, there has been a growing interest in using rich representations such as relational ...
A fundamental problem in reinforcement learning is balancing exploration and exploitation. We addres...
Abstract. Reinforcement learning, and Q-learning in particular, encounter two major problems when de...
Relational reinforcement learning is presented, a learning technique that combines reinforcement lea...
Relational reinforcement learning (RRL) is a Q-learning technique which uses first or-der regression...
Relational Reinforcement Learning (RRL) is a subfield of machine learning in which a learning agent ...
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...
In recent years, there has been a growing interest in using rich representations such as relational...
We present a perspective and challenges for Relational Reinforcement Learning (RRL). We first survey...
Abstract. In reinforcement learning, an agent tries to learn a policy, i.e., how to select an action...
We propose a novel method for reinforcement learning in domains that are best described using relati...
International audienceIn this work, we introduce the first approach to the Inverse Reinforcement Lea...
RRL is a relational reinforcement learning system based on Q-learning in relational state-action spa...
This article describes a learning classifier system (LCS) approach to relational reinforcement learn...
In recent years, there has been a growing interest in using rich representations such as relational ...
A fundamental problem in reinforcement learning is balancing exploration and exploitation. We addres...