67 search results - page 4 / 14 » Learning predictive state representations using non-blind po... |
111
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AAAI
2006
15 years 1 months ago
2006
We discuss the problem of finding a good state representation in stochastic systems with observations. We develop a duality theory that generalizes existing work in predictive sta...
ICML
2009
IEEE
16 years 15 days ago
2009
IEEE
We propose a new neural network architecture, called Simple Recurrent Temporal-Difference Networks (SR-TDNs), that learns to predict future observations in partially observable en...
APIN
2004
14 years 11 months ago
2004
In this paper we are concerned with the problem of learning how to solve planning problems in one domain given a number of solved instances. This problem is formulated as the probl...
FLAIRS
2004
15 years 1 months ago
2004
er provides new techniques for abstracting the state space of a Markov Decision Process (MDP). These techniques extend one of the recent minimization models, known as -reduction, ...
180
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ICCCI
2011
Springer
13 years 11 months ago
2011
Springer
Multiagent reinforcement learning problems are especially difficult because of their dynamism and the size of joint state space. In this paper a new benchmark problem is proposed, ...