The pole-balancing example is from Michie and Chambers (1968) and. Barto, Sutton, and .... The golf example was suggested by Chris Watkins. References.
Course Notes
The Reinforcement Learning Problem Richard S. Sutton and Andrew G. Barto c All rights reserved In this chapter we introduce the problem that we try to solve in the rest of the book. For us, this problem denes the eld of reinforcement learning: any method that is suited to solving this problem we consider to be a reinforcement learning method. Our objective in this chapter is to describe the reinforcement learning problem in a broad sense. We try to convey the wide range of possible applications that can be framed as reinforcement learning tasks. We also describe mathematically idealized forms of the reinforcement learning problem for which precise theoretical statements can be made. We introduce key elements of the problem's mathematical structure, such as value functions and Bellman equations. As in all of articial intelligence, there is a tension between breadth of applicability and mathematical tractability. In this chapter we introduce this tension and discuss some of the trade-os and challenges that it implies.
1 The Agent{Environment Interface The reinforcement learning problem is meant to be a straightforward framing of the problem of learning from interaction to achieve a goal. The learner and decision-maker is called the agent. The thing it interacts with, comprising everything outside the agent, is called the environment. These interact continually, the agent selecting actions and the environment responding to those actions and presenting new situations to the agent.1 The environment also gives rise to rewards, special numerical values that the agent tries to maximize over time. A complete specication of an environment denes a task , one instance of the reinforcement learning problem. These course notes are chapters from a textbook, Reinforcement Learning: An Introduction, by Richard S. Sutton and Andrew G. Barto, to be published by MIT Press in January, 1998. 1 We use the terms agent, environment, and action instead of the engineers' terms controller, controlled system (or plant ), and control signal because they are meaningful to a wider audience.
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Figure 1: The agent{environment interaction in reinforcement learning. More specically, the agent and environment interact at each of a sequence of discrete time steps, t = 0 1 2 3 : : :.2 At each time step t, the agent receives some representation of the environment's state, s 2 S , where S is the set of possible states, and on that basis selects an action, a 2 A(s ), where A(s ) is the set of actions available in state s . One time step later, in part as a consequence of its action, the agent receives a numerical reward , r +1 2
