Quad-Q-learning.
This paper develops the theory of quad-Q-learning which is a new 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. Although Q-learning and quad-Q-learning are in many respects similar, they differ in their notion of state transitions. 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. If four new states result, then each new state is treated independently as if four new environments resulted. If no new state results, no further action is taken in the associated environment. 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. Quad-Q-learning can be readily generalized to a family of n-Q-learning algorithms which are applicable to learning how to partition large intractable problem domains into smaller problems that can be solved independently. 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. The problem with the discrete case is that learning for one state does not improve learning with respect to other nearby states nor does it generalize to previously unseen 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.
Clausen
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Department of Computer Science, George Mason University, Fairfax, VA 22030, USA.
Wechsler
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