A Path-Planning Approach Based on Potential and Dynamic Q-Learning for Mobile Robots in Unknown Environment

Algorithm 1

Classical Q-learning pseudocode.

(1)	Initiate and in Q-table to zero
(2)	Select a starting state
(3)	for each step of episode do
(4)	while   is not terminal do
(5)	Select and execute it
(6)	Receive an immediate reward
(7)	Observe the next state
(8)	Update the table entry by
(9)
(10)
(11)	Until is the terminal state
(12)	end while
(13)	end for
(14)	Output optimal policy