Q-learning
A major approach to solve games has been the Q-learning approach. In order to fully understand the approach, a basic example will illustrate a simplistic case where the number of states of the environment is limited to 6, state 0 is the entrance, state 5 is the exit. At each stage, some actions make it possible to jump to another state, as described in the following figure:
The reward is, let's say, 100, when the agent leaves state 4 to state 5. There isn't any other reward for other states since the goal of the game in this example is to find the exit. The reward is time-delayed and the agent has to scroll through multiple states from state 0 to state 4 to find the exit.
In this case, Q-learning consists of learning a matrix Q, representing the value of a state-action pair:
- Each row in the Q-matrix corresponds to a state the agent would be in
- Each column the target state from that state
the value representing how much choosing that action in that state will move us close...
