ICMLA
2004
14 years 1 months ago
2004
We present JoSTLe, an algorithm that performs value iteration on control problems with continuous actions, allowing this useful reinforcement learning technique to be applied to p...
ICAI
2004
14 years 1 months ago
2004
An explicit exploration strategy is necessary in reinforcement learning (RL) to balance the need to reduce the uncertainty associated with the expected outcome of an action and the...
ESANN
2006
14 years 1 months ago
2006
We focus on neuro-dynamic programming methods to learn state-action value functions and outline some of the inherent problems to be faced, when performing reinforcement learning in...
HIS
2004
14 years 1 months ago
2004
: This work presents a new hybrid neuro-fuzzy model for automatic learning of actions taken by agents. The main objective of this new model is to provide an agent with intelligence...
AIIDE
2006
14 years 1 months ago
2006
Reinforcement learning is an effective machine learning paradigm in domains represented by compact and discrete state-action spaces. In high-dimensional and continuous domains, ti...
AIIDE
2006
14 years 1 months ago
2006
This paper investigates the challenges posed by the application of reinforcement learning to large-scale strategy games. In this context, we present steps and techniques which syn...
AAAI
2006
14 years 1 months ago
2006
My research attempts to address on-line action selection in reinforcement learning from a Bayesian perspective. The idea is to develop more effective action selection techniques b...
AAAI
2006
14 years 1 months ago
2006
We examine the problem of Transfer in Reinforcement Learning and present a method to utilize knowledge acquired in one Markov Decision Process (MDP) to bootstrap learning in a mor...
UAI
2008
14 years 1 months ago
2008
Model-based Bayesian reinforcement learning has generated significant interest in the AI community as it provides an elegant solution to the optimal exploration-exploitation trade...
NIPS
2007
14 years 1 months ago
2007
We present four new reinforcement learning algorithms based on actor-critic and natural-gradient ideas, and provide their convergence proofs. Actor-critic reinforcement learning m...