46 search results - page 5 / 10 » Measuring and Optimizing Behavioral Complexity for Evolution... |
163
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ATAL
2005
Springer
15 years 9 months ago
2005
Springer
Reinforcement learning problems are commonly tackled with temporal difference methods, which use dynamic programming and statistical sampling to estimate the long-term value of ta...
112
Voted
AAAI
2010
15 years 5 months ago
2010
We consider the problem of incorporating end-user advice into reinforcement learning (RL). In our setting, the learner alternates between practicing, where learning is based on ac...
182
Voted
LAMAS
2005
Springer
15 years 9 months ago
2005
Springer
Abstract Multi-agent systems (MASs) is an area of distributed artificial intelligence that emphasizes the joint behaviors of agents with some degree of autonomy and the complexiti...
133
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EUROCAST
2007
Springer
15 years 9 months ago
2007
Springer
Abstract a paradigm of modern Machine Learning (ML) which uses rewards and punishments to guide the learning process. One of the central ideas of RL is learning by “direct-online...
155
Voted
ATAL
2008
Springer
15 years 5 months ago
2008
Springer
Decentralized Markov decision processes are frequently used to model cooperative multi-agent systems. In this paper, we identify a subclass of general DEC-MDPs that features regul...