强化学习的ppt3.pptVIP

REINFORCEMENT LEARNING Overview Applications to Music rise of the machine ‘ let us assume that we are playing against an imperfect player, one whose play is sometimes incorrect and allows us to win. For the moment, in fact, let us consider draws and losses to be equally bad for us. How might we construct a player that will find the imperfections in its opponents play and learn to maximize its chances of winning?’ - Sutton, R. S., and A. G. Barto. 1998. Reinforcement Learning: An Introduction Goals topics What is Reinforcement Learning? History, Introduction, Individuality Examples Elements of a Reinforcement Learning System The Reinforcement Problem - An Example Applications to Music Questions Comments History heterostatic theory of adaptive systems developed by A. Harry Klopf ‘ but in 1979 we came to realize that perhaps the simplest of the ideas, which had long been taken for granted, had received surprisingly little attention from a computational perspective. This was simply the idea of a learning system that wants something, that adapts its behaviour in order to maximize a special signal from its environment. This was the idea of a hedonistic learning system, or, as we would say now, the idea of reinforcement learning’ - Sutton, R. S., and A. G. Barto. 1998. Reinforcement Learning: An Introduction Introduction What is Reinforcement Learning?‘ Reinforcement learning is learning what to do--how to map situations to actions--so as to maximize a numerical reward signal’  - Sutton, R. S., and A. G. Barto. 1998. Reinforcement Learning: An Introduction These two characteristics: trial-and-error delayed rewardThese are the two most important distinguishing features of reinforcement learning  Introduction The formulation is intended to include just these three aspects- sensation- action- goal ‘ Clearly, such an agent must be able to sense the state of the environment to some extent and must be able to take