175 search results - page 8 / 35 » Forgetting Reinforced Cases |
180
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JMLR
2012
13 years 6 months ago
2012
Contextual bandit learning is a reinforcement learning problem where the learner repeatedly receives a set of features (context), takes an action and receives a reward based on th...
137
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AAAI
1993
15 years 5 months ago
1993
This paper analyzes the complexity of on-line reinforcement learning algorithms, namely asynchronous realtime versions of Q-learning and value-iteration, applied to the problem of...
129
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ICML
2010
IEEE
15 years 5 months ago
2010
IEEE
We consider the problem of multi-task reinforcement learning where the learner is provided with a set of tasks, for which only a small number of samples can be generated for any g...
157
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GECCO
2005
Springer
15 years 10 months ago
2005
Springer
Many real-world problems are multi-objective optimization problems and evolutionary algorithms are quite successful on such problems. Since the task is to compute or approximate t...
147
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LFCS
2009
Springer
15 years 9 months ago
2009
Springer
We extend the alternating-time temporal logics ATL and ATL with strategy contexts and memory constraints: the first extension makes strategy quantifiers to not “forget” the s...