000			02050cam a22003978i 4500
999			_c93540 _d93540
005			20251120114619.0
007			cr aa aaaaa
008			241030s2025 nju 001 0 eng c
010			_a 2024049767
020			_a9781394206452 _q(hardback)
020			_z9781394206469 _q(adobe pdf)
020			_z9781394206476 _q(epub)
035			_a23909143
040			_aWaSeSS/DLC _beng _erda _cDLC
041			_aeng
042			_apcc
050	0	0	_aQ325.6 _b.R34 2025
082	0	0	_a006.3/1 _223/eng/20241209
100	1		_aRahman, Abdul _c(Executive), _eauthor.
245	1	0	_aReinforcement learning for cyber operations : _bapplications of Artificial Intelligence for penetration testing / _cAbdul Rahman [and five others].
263			_a2502
264		1	_aHoboken, New Jersey : _bWiley, _c[2025]
300			_a1 online resource
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
500			_aIncludes index.
520			_a"Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs from supervised learning in not needing labelled input/output pairs to be presented, and in not needing sub-optimal actions to be explicitly corrected. Instead the focus is on finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge)."-- _cProvided by publisher.
650		0	_aReinforcement learning.
650		0	_aPenetration testing (Computer security)
655		4	_aElectronic books.
856	4	0	_uhttps://onlinelibrary.wiley.com/doi/book/10.1002/9781394206483 _yFull text is available at Wiley Online Library Click here to view
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2ddc _cER