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Chapter 11
And unlike unsupervised learning, the agent's goal is not to find some inherent
structure in the input (the learning may find some structure, but that isn't the
goal); instead, its only goal is to maximize the rewards (in the long run) and reduce
the punishments.
RL lingo
Before learning various RL algorithms, it is important we understand a few
important terms. We will illustrate the terms with the help of two examples, first a
robot in a maze, and second an agent controlling the wheels of a self-driving car. The
two RL agents are shown as follows:
• State, S: State is the set of tokens (or representations) that can define all of the
possible states the environment can be in. It can be continuous or discrete.
In the case of the robot finding its path through a maze, the state can be
represented by a 4 × 4 array, with elements telling whether that block is empty
or occupied or blocked. A block with a value of 1 means it is occupied by the
robot, 0 means it is empty, and X represents that the block is impassable. Each
element in this array S, can have one of the three discrete values, so the state is
discrete in nature. Next, consider the agent controlling the steering wheel of a
self-driving car. The agent takes as an input the front view image. The image
contains continuous valued pixels, so here the state is continuous.
• Action, A(S): Actions are the set of all possible things that the agent can do in
a particular state. The set of possible actions, A, depends on the present state,
S. Actions may or may not result in a change of state. Like states, they can
be discrete or continuous. The robot finding a path in the maze can perform
five discrete actions [up, down, left, right, no change]. The SDC agent, on the
other hand, can rotate the steering wheel in a continuous range of angles.
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