State = [
isDirectionRight, isDirectionLeft, isDirectionDown, isDirectionUp,
isAppleRight, isAppleLeft, isAppleDown, isAppleUp,
isCollidingRight, isCollidingLeft, isCollidingDown, isCollidingUp
]
Action in [0, 1, 2, 3]
dY, dX = [[0, 1], [0, -1], [1, 0], [-1, 0]][Action] <--> dY, dX = [Right, Left, Down, Up][Action]
// Exploration
if random() < epsilon
choose random action
// Exploitation
else
choose action that maximizes Q[current_state][action]- The more the AI trains, the more rewards we get (see console.log)
- The Q table and training infos are persisted in local storage
