Transcription of Lecture 2: Markov Decision Processes - David Silver
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Lecture 2: Markov Decision Processes - David Silver
Lecture 2: Markov Decision ProcessesLecture 2: Markov Decision ProcessesDavid SilverLecture 2: Markov Decision Processes1 Markov Processes2 Markov Reward Processes3 Markov Decision Processes4 Extensions to MDPsLecture 2: Markov Decision ProcessesMarkov ProcessesIntroductionIntroduction to MDPsMarkov Decision processesformally describe an environmentfor reinforcement learningWhere the environment isfully The currentstatecompletely characterises the processAlmost all RL problems can be formalised as MDPs, control primarily deals with continuous MDPsPartially observable problems can be converted into MDPsBandits are MDPs with one stateLecture 2: Markov Decision ProcessesMarkov ProcessesMarkov PropertyMarkov Property The future is independent of the past given the present DefinitionA stateStisMarkovif and only ifP[St+1|St] =P[St+1|S1.]
Lecture 2: Markov Decision Processes Markov Processes Markov Chains Markov Process A Markov process is a memoryless random process, i.e. a sequence of random states S 1;S 2;:::with the Markov property. De nition A Markov Process (or Markov Chain) is a tuple hS;Pi Sis a ( nite) set of states Pis a state transition probability matrix, P ss0= P[S ...
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