1233 search results - page 198 / 247 » Reinforcement learning |
122
Voted
ICML
2009
IEEE
16 years 4 months ago
2009
IEEE
In this paper we introduce the first algorithms for efficiently learning a simulation policy for Monte-Carlo search. Our main idea is to optimise the balance of a simulation polic...
156
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ICML
2001
IEEE
16 years 4 months ago
2001
IEEE
Direct policy search is a practical way to solve reinforcement learning problems involving continuous state and action spaces. The goal becomes finding policy parameters that maxi...
131
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ECML
2007
Springer
15 years 10 months ago
2007
Springer
We present Policy Gradient Actor-Critic (PGAC), a new model-free Reinforcement Learning (RL) method for creating limited-memory stochastic policies for Partially Observable Markov ...
149
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NIPS
2008
15 years 5 months ago
2008
Policy gradient (PG) reinforcement learning algorithms have strong (local) convergence guarantees, but their learning performance is typically limited by a large variance in the e...
143
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IIE
2007
15 years 3 months ago
2007
Conventional wisdom attributes the lack of effective technology use in classrooms to a shortage of professional development or poorly run professional development. At the same time...