Add to ORKGThis doctoral thesis deals with the development of a function approximator and its application to methods for learning discrete and continuous actions: 1. A general function approximator ? Locally Weighted Interpolating Growing Neural Gas (LWIGNG) ? is developed from Growing Neural Gas (GNG). The topological neighbourhood structure is used for calculating interpolations between neighbouring neurons and for applying a local weighting scheme. The capabilities of this method are shown in several experiments, with special considerations given to changing target functions and changing input distributions. 2. To learn discrete actions LWIGNG is combined with Q-Learning forming the Q-LWIGNG method. The underlying GNG-algorithm h...
In this paper, we propose a new approach to function approximation based on a growing neural gas (GN...
For complex systems, reinforcement learning has to be generalised from a discrete form to a continuo...
Reinforcement learning is a machine learning paradigm that deals with optimisation and learns by int...
This doctoral thesis deals with the development of a function approximator and its application to m...
Many interesting problems in reinforcement learning (RL) are continuous and/or high dimensional, and...
Many interesting problems in reinforcement learning (RL) are continuous and/or high dimensional, and...
Reinforcement learning problems are commonly tackled with temporal difference methods, which use dyn...
AbstractThis work presents the restricted gradient-descent (RGD) algorithm, a training method for lo...
This is the version of record. It originally appeared on arXiv at http://arxiv.org/abs/1603.00748.Mo...
Reinforcement learning is a general and powerful way to formulate complex learning problems and acqu...
Reinforcement learning is defined as the problem of an agent that learns to perform a certain task t...
Self-learning controllers offer various strong benefits over conventional controllers, the most impo...
International audienceIn this paper, we propose a contribution in the field of Reinforcement Learnin...
This thesis is a study of practical methods to estimate value functions with feedforward neural netw...
The application of reinforcement learning to problems with continuous domains requires representing ...
In this paper, we propose a new approach to function approximation based on a growing neural gas (GN...
For complex systems, reinforcement learning has to be generalised from a discrete form to a continuo...
Reinforcement learning is a machine learning paradigm that deals with optimisation and learns by int...
This doctoral thesis deals with the development of a function approximator and its application to m...
Many interesting problems in reinforcement learning (RL) are continuous and/or high dimensional, and...
Many interesting problems in reinforcement learning (RL) are continuous and/or high dimensional, and...
Reinforcement learning problems are commonly tackled with temporal difference methods, which use dyn...
AbstractThis work presents the restricted gradient-descent (RGD) algorithm, a training method for lo...
This is the version of record. It originally appeared on arXiv at http://arxiv.org/abs/1603.00748.Mo...
Reinforcement learning is a general and powerful way to formulate complex learning problems and acqu...
Reinforcement learning is defined as the problem of an agent that learns to perform a certain task t...
Self-learning controllers offer various strong benefits over conventional controllers, the most impo...
International audienceIn this paper, we propose a contribution in the field of Reinforcement Learnin...
This thesis is a study of practical methods to estimate value functions with feedforward neural netw...
The application of reinforcement learning to problems with continuous domains requires representing ...
In this paper, we propose a new approach to function approximation based on a growing neural gas (GN...
For complex systems, reinforcement learning has to be generalised from a discrete form to a continuo...
Reinforcement learning is a machine learning paradigm that deals with optimisation and learns by int...