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RL algorithms consist of four main components: a model generator, calculation ofthe reward/punishment, learning, and an updating strategy. The model generator isresponsible for generating a model that includes the states and actions of the task,the cost function, and the reward collection. The reward and punishment can bereal or fictitious (e.g., as simulated by the computer). The learning component isresponsible for synthesizing a strategy to solve the task. The updating strategycan be static (i.e., fixed) or dynamic (i.e., varying with the amount of timeremaining before completion). The updating strategy can be selected randomly orcan be problem specific. The learning component is responsible for determining theappropriate strategy. The strategy can be determined based on existing RL algorithms (for example,the value function can be given arbitrarily), the objective function in optimization(for example, minimization), or knowledge about the reward/punishment and costfunction.
Negative feedback algorithms, in turn, operate as negative feedback controllersusing. The negative feedback controller receives a truncated estimate of the controlledvariable from a process or model and uses the estimate to adjust inputs to reduceerrors. See Saunders Chapter 2.48, “Negative Feedback Control.”
Of interest in RL is the class of algorithms known as model free prediction orneigborhood algorithms R1-R9,85 i.e., model free RL algorithms. The algorithms usually work inan environment of states s and actions a modeled by a state-action probability that is locallyobservable. An advantage of these algorithms is that they need not model the environment.On the other hand, they do not learn to predict a state sequence, or at least, theirlearning cycle is independent of the environment. Thus theytypically require special programming if used in an environment where the sequenceof states is known or where there is no predictable block structure to sequence over.Nonetheless, the discussion in this chapter is not limited to the model-free RL class.Instead, we consider a more general class of learning algorithms and make no additionalassumptions about motor function, modeling, etc. d2c66b5586