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Reinforcement Learning
So, the first question that arises is what is RL and how is it different from supervised
and unsupervised learning? Anyone who owns a pet knows that the best strategy to
train a pet is rewarding it for desirable behavior and punishing it for bad behavior.
RL, also called learning with a critic, is a learning paradigm where the agent
learns in the same manner. The agent here corresponds to our network (program);
it can perform a set of Actions (a), which brings about a change in the State (s)
of the environment and, in turn, the agent receives a reward or punishment from
the environment.
For example, consider the case of training a dog to fetch the ball: here, the dog is
our agent, the voluntary muscle movements that the dog makes are the actions,
and the ground (including person and ball) is the environment; the dog perceives
our reaction to its action in terms of giving it a bone as a reward. RL can be defined
as a computational approach to goal-directed learning and decision making, from
interaction with the environment, under some idealized conditions. The Agent can
sense the state of the Environment, and the Agent can perform specific well-defined
actions on the Environment. This causes two things: first, a change in the state of the
environment, and second, a reward is generated (under ideal conditions). This cycle
continues, and in theory the agent learns how to more frequently generate a reward
over time:
Unlike supervised learning, the Agent is not presented with any training examples; it
does not know what the correct action is.
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