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Oleh: Wechsler, H. ; Clausen, C.
Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 11 no. 2 (2000), page 279-294.
Topik: LEARNING; quad - q - learning
  • Perpustakaan Pusat (Semanggi)
    • Nomor Panggil: II36.4
    • Non-tandon: 1 (dapat dipinjam: 0)
    • Tandon: tidak ada
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Isi artikelDevelops the theory of quad-Q - learning which is a learning algorithm that evolved from Q - learning. Quad - Q - learning is applicable to problems that can be solved by “divide and conquer” techniques. Quad - Q - learning concerns an autonomous agent that learns without supervision to act optimally to achieve specified goals. The learning agent acts in an environment that can be characterized by a state. In the Q - learning environment, when an action is taken, a reward is received and a single new state results. The objective of Q - learning is to learn a policy function that maps states to actions so as to maximize a function of the rewards such as the sum of rewards. However, with respect to quad - Q - learning, when an action is taken from a state either an immediate reward and no new state results, or no reward is received and four new states result from taking that action. The environment in which quad - Q - learning operates can thus be viewed as a hierarchy of states where lower level states are the children of higher level states. The hierarchical aspect of quad - Q - learning leads to a bottom up view of learning that improves the efficiency of learning at higher levels in the hierarchy. The objective of quad - Q - learning is to maximize the sum of rewards obtained from each of the environments that result as actions are taken. Two versions of quad - Q - learning are discussed ; these are discrete state and mixed discrete and continuous state quad - Q - learning. The discrete state version is only applicable to problems with small numbers of states. Scaling up to problems with practical numbers of states requires a continuous state learning method. Continuous state learning can be accomplished using functional approximation methods. Application of quad - Q - learning to image compression is briefly described.
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