181 search results - page 12 / 37 » State Space Reduction For Hierarchical Reinforcement Learnin... |
ICML
2006
IEEE
14 years 8 months ago
2006
IEEE
For a Markov Decision Process with finite state (size S) and action spaces (size A per state), we propose a new algorithm--Delayed Q-Learning. We prove it is PAC, achieving near o...
NIPS
2007
13 years 9 months ago
2007
We provide a provably efficient algorithm for learning Markov Decision Processes (MDPs) with continuous state and action spaces in the online setting. Specifically, we take a mo...
ICML
1994
IEEE
13 years 11 months ago
1994
IEEE
Reinforcement learning (RL) algorithms provide a sound theoretical basis for building learning control architectures for embedded agents. Unfortunately all of the theory and much ...
AGENTS
2001
Springer
13 years 12 months ago
2001
Springer
This paper describes Icarus, an agent architecture that embeds a hierarchical reinforcement learning algorithm within a language for specifying agent behavior. An Icarus program e...
AAAI
2008
13 years 9 months ago
2008
Hierarchical state decompositions address the curse-ofdimensionality in Q-learning methods for reinforcement learning (RL) but can suffer from suboptimality. In addressing this, w...