Abstract
In the previous chapter, we presented an algorithm LoE-AIM that models memory-bounded agents assuming that the memory size of these agents is known beforehand. In situations where such prior knowledge is unavailable, a possible solution can be to use a very large memory size that suffices to be a conservative upper-bound of the true unknown memory size.
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© 2014 Springer International Publishing Switzerland
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Chakraborty, D. (2014). Convergence, Targeted Optimality and Safety in Multiagent Learning. In: Sample Efficient Multiagent Learning in the Presence of Markovian Agents. Studies in Computational Intelligence, vol 523. Springer, Cham. https://doi.org/10.1007/978-3-319-02606-0_4
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DOI: https://doi.org/10.1007/978-3-319-02606-0_4
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-02605-3
Online ISBN: 978-3-319-02606-0
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