215 search results - page 3 / 43 » Model-Based Reinforcement Learning with Continuous States an... |
AAAI
1998
13 years 8 months ago
1998
Reinforcement learning is an effective technique for learning action policies in discrete stochastic environments, but its efficiency can decay exponentially with the size of the ...
AAMAS
2007
Springer
14 years 1 months ago
2007
Springer
Abstract. Reinforcement learning (RL) is a widely used learning paradigm for adaptive agents. There exist several convergent and consistent RL algorithms which have been intensivel...
ICRA
2009
IEEE
14 years 2 months ago
2009
IEEE
Abstract— Continuous action sets are used in many reinforcement learning (RL) applications in robot control since the control input is continuous. However, discrete action sets a...
ICML
2009
IEEE
14 years 8 months ago
2009
IEEE
Reinforcement Learning methods for controlling stochastic processes typically assume a small and discrete action space. While continuous action spaces are quite common in real-wor...
ICML
2010
IEEE
13 years 8 months ago
2010
IEEE
We introduce new, efficient algorithms for value iteration with multiple reward functions and continuous state. We also give an algorithm for finding the set of all nondominated a...