Add to ORKGA hierarchical representation of the input-output transition function in a learning system is suggested. The choice of either representing the knowledge in a learning system as a discrete set of input-output pairs or as a continuous input-output transition function is discussed. The conclusion that both representations could be efficient, but at different levels of abstraction is made. The difference between strategies and actions is defined. An algorithm for using adaptive critic methods in a two-level reinforcement learning system is presented. Simulations of a one dimensional hierarchical reinforcement learning system is presented
The aim of this thesis is to create precise computational models of how humans create and use hierar...
This chapter introduces a novel hierarchical adaptive critic design to improve learning and optimiza...
Abstract. Reinforcement learning is bedeviled by the curse of dimensionality: the number of paramete...
A hierarchical representation of the input-output transition function in a learning system is sugges...
This paper describes two methods for hierarchically organizing temporal behaviors. The first is more...
We propose a model-based approach to hierarchical reinforcement learning that exploits shared knowle...
There is an increasing interest in Reinforcement Learning to solve new and more challenging problems...
There is an increasing interest in Reinforcement Learning to solve new and more challenging problems...
There is an increasing interest in Reinforcement Learning to solve new and more challenging problems...
There is an increasing interest in Reinforcement Learning to solve new and more challenging problems...
There is an increasing interest in Reinforcement Learning to solve new and more challenging problems...
There is an increasing interest in Reinforcement Learning to solve new and more challenging problems...
This paper presents a new approach to hierarchical reinforcement learning based on the MAXQ decompos...
Reinforcement learning provides a means for autonomous agents to improve their action selection stra...
The aim of this thesis is to create precise computational models of how humans create and use hierar...
The aim of this thesis is to create precise computational models of how humans create and use hierar...
This chapter introduces a novel hierarchical adaptive critic design to improve learning and optimiza...
Abstract. Reinforcement learning is bedeviled by the curse of dimensionality: the number of paramete...
A hierarchical representation of the input-output transition function in a learning system is sugges...
This paper describes two methods for hierarchically organizing temporal behaviors. The first is more...
We propose a model-based approach to hierarchical reinforcement learning that exploits shared knowle...
There is an increasing interest in Reinforcement Learning to solve new and more challenging problems...
There is an increasing interest in Reinforcement Learning to solve new and more challenging problems...
There is an increasing interest in Reinforcement Learning to solve new and more challenging problems...
There is an increasing interest in Reinforcement Learning to solve new and more challenging problems...
There is an increasing interest in Reinforcement Learning to solve new and more challenging problems...
There is an increasing interest in Reinforcement Learning to solve new and more challenging problems...
This paper presents a new approach to hierarchical reinforcement learning based on the MAXQ decompos...
Reinforcement learning provides a means for autonomous agents to improve their action selection stra...
The aim of this thesis is to create precise computational models of how humans create and use hierar...
The aim of this thesis is to create precise computational models of how humans create and use hierar...
This chapter introduces a novel hierarchical adaptive critic design to improve learning and optimiza...
Abstract. Reinforcement learning is bedeviled by the curse of dimensionality: the number of paramete...