162 search results - page 12 / 33 » Off-Policy Temporal Difference Learning with Function Approx... |
PKDD
2009
Springer
15 years 4 months ago
2009
Springer
The goal of approximate policy evaluation is to “best” represent a target value function according to a specific criterion. Temporal difference methods and Bellman residual m...
AAAI
2008
14 years 11 months ago
2008
Off-policy reinforcement learning is aimed at efficiently reusing data samples gathered in the past, which is an essential problem for physically grounded AI as experiments are us...
ICMLA
2008
14 years 11 months ago
2008
In reinforcement learning, it is a common practice to map the state(-action) space to a different one using basis functions. This transformation aims to represent the input data i...
SOFTWARE
2002
14 years 9 months ago
2002
We consider the problem of characterisation of sequences of heterogeneous symbolic data that arise from a common underlying temporal pattern. The data, which are subject to impreci...
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EH
1999
IEEE
1999
IEEE
Evolvable Hardware or Learning Hardware? Induction of State Machines from Temporal Logic Constraints
15 years 1 months ago
Here we advocate an approach to learning hardware based on induction of finite state machines from temporal logic constraints. The method involves training on examples, constraint...