Objective
The goal of the project is to program an agent to learn a route, through a maze, based on rewards. The target location is a rewarded location in that maze, and the agent will learn to navigate from a fixed start to this rewarded target via exploration and repetition.
For this, the approach taken is based on Reinforcement Learning (RL). The aim is to implement a model-free off-policy version of RL called Q-Learning, using a tabular representation for Q(s,a), i.e., a matrix with two dimensions S and A; S being all possible states (the locations in the maze), and A being all actions possible in each state (up, down, left, right).
A value in the matrix at location (s,a) represents the Q(s,a) value, i.e., the value of an action a in state s.
Essentially, the problem to solve boils down to learning a S x A matrix of values with each value representing the utility of an s,a pair.
Every step the agent takes updates one such value, namely the last visited (s,a) pair. As Q-learning is used, and Q-learning is off-policy (thus, updates are not based on actual actions chosen), we will update this value using the value of the best possible next action Q(s',a.max).
The full update rule is:
Q(s,a).new = Q(s,a).old + a(r + yQ.max(s',a.max) - Q(s,a).old)
Where Q.max(s',a.max) is the Q-value of the best action so far in state s'.
The agent learns by taking random actions, and after each action updating Q(s,a). I will experiment with action selection (the policy) and it will be implemented with the e-Greedy algorithm.
This method will take either a random action with a probability of e (exploration) or a greedy action with a probability of 1 - e (exploitation). A random action is selected out of the 4 possible actions while a greedy action is an action with the highest predicted value so far, so it chooses the action with the highest Q(s,a) for the states we are now in.
Typically we find that with a high e-value, though the expected reward on a step might be lower, the total reward in the long-term is greater.