Abstract
Reinforcement learning is became one of the most
important approaches to machine intelligence. Now RL
is widely use by different research field as intelligent
control, robotics and neuroscience. It provides us
possible solution within unknown environment, but at the
same time we have to take care of its decision because
RL can independently learn without prior knowledge or
training and it take decision by learning experience
through trial-and-error interaction with its environment.
In recent time many research works was done for RL
and researchers has also proposed various algorithm
and model such as SARSA [2], TDN [3] which tries to
solve sequential decision making problems of continuous
state and action space.
In this paper we proposed Q-learning algorithm and
evaluation of RL techniques (Reinforcement learning
architecture, algorithms for making training matrix in
the form of state-action pair Q-table) containing learner
(decision making agent) that takes actions in an
environment and receive reward for (or penalty) its
actions in trying to solves a problems. Learning agent,
the fundamental element of reinforcement learning,
there is a decision maker that receive and select an
action for the system.
In reinforcement learning technique especially in Query
base self learning the learner (Agent) required a lot of
training input of execution cycle. In order to assess and
comparison of QA and TDN based reinforcement
learning, we found that QA is better in the context of
discount rate, learning time, memory usage.