13 search results - page 2 / 3 » Continuous-State Reinforcement Learning with Fuzzy Approxima... |
NIPS
2007
13 years 8 months ago
2007
Learning in real-world domains often requires to deal with continuous state and action spaces. Although many solutions have been proposed to apply Reinforcement Learning algorithm...
FUZZIEEE
2007
IEEE
14 years 1 months ago
2007
IEEE
— Reinforcement learning (RL) is a learning control paradigm that provides well-understood algorithms with good convergence and consistency properties. Unfortunately, these algor...
ATAL
2009
Springer
14 years 1 months ago
2009
Springer
AIIDE
2006
13 years 8 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...
NIPS
1996
13 years 8 months ago
1996
Dynamic Programming, Q-learning and other discrete Markov Decision Process solvers can be applied to continuous d-dimensional state-spaces by quantizing the state space into an arr...