Essentially, Q-learning lets the agent use the environment's rewards to learn, over time, the best action to take in a given state. In our Taxi environment, we have the reward table, P, that the agent will learn from. It does thing by looking receiving a reward for taking an action in the current state, then updating a Q-value to remember if that action was beneficial. The values store in the Q-table are called a Q-values, and they map to a (state, action) combination. A Q-value for a particular state-action combination is representative of the "quality" of an action taken from that state.

Artificial Intelligence promises to benefit humankind in unprecedented ways. But small states are especially vulnerable to the technology's downside short of strengthening social cohesion and resilience. Artificial intelligence or AI, broadly defined as human-like intelligence and qualities exhibited by machines, has made a huge technological leap since 1956 when the term was first coined. Tech giants like Google and IBM believe that AI will benefit mankind in unprecedented ways. For example, autonomous vehicles are expected to enhance both traffic safety and flow whereas care-bots will aid in areas such as elderly and patient care.