56 search results - page 6 / 12 » Q-Learning in Continuous State and Action Spaces |
158
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ASE
2004
15 years 5 months ago
2004
The verification of concurrent systems through an exhaustive traversal of the state space suffers from the infamous state-space-explosion problem, caused by the many interleavings ...
145
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ICML
2003
IEEE
16 years 6 months ago
2003
IEEE
Hierarchical reinforcement learning is a general framework which attempts to accelerate policy learning in large domains. On the other hand, policy gradient reinforcement learning...
150
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ICML
2006
IEEE
16 years 6 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...
173
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IROS
2008
IEEE
16 years 6 days ago
2008
IEEE
— A long cherished goal in artificial intelligence has been the ability to endow a robot with the capacity to learn and generalize skills from watching a human teacher. Such an ...
152
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UAI
2000
15 years 7 months ago
2000
We propose a new approach to the problem of searching a space of policies for a Markov decision process (MDP) or a partially observable Markov decision process (POMDP), given a mo...