In his doctoral thesis, Learning from delayed rewards, Watkins introduced the concept of Q-learning in the year 1989. The goal of Q-learning is to learn an optimal action selection policy. Given a specific state, s, and taking a specific action, a, Q-learning attempts to learn the value of the state s. In its simplest version, Q-learning can be implemented with the help of look-up tables. We maintain a table of values for every state (row) and action (column) possible in the environment. The algorithm attempts to learn the value—that is, how good it is to take a particular action in the given state.
We start by initializing all of the entries in the Q-table to 0; this ensures all states a uniform (and hence equal chance) value. Later, we observe the rewards obtained by taking a particular action and, based on the rewards, we update the Q-table. The update in...
