import gym
import universe
import random


#reinforcement learning step
def determine_turn(turn, observation_n, j, total_sum, prev_total_sum, reward_n):
    #for every 15 iterations, sum the total observations, and take the average
    #if lower than 0, change the direction
    #
    #

    if(j >= 15):
        if(total_sum/ j) == 0:
            turn = True
        else:
            turn = False

        #reset vars
        prev_total_sum = total_sum
        total_sum = 0
        j = 0

    else:
        turn = False
    if(observation_n != None):
        #increment the counter and reward sum
        j+=1
        total_sum += reward_n
    return(turn, j, total_sum, prev_total_sum)




def main():

    env = gym.make('flashgames.CoasterRacer-v0')
    observation_n = env.reset()

    #num of game iterations
    n = 0
    j = 0

    #sum of observations
    total_sum = 0
    prev_total_sum = 0

    #define our turns or keuboard actions
    left = [('KeyEvent', 'ArrowUp', True), ('KeyEvent', 'ArrowLeft', True), ('KeyEvent', 'ArrowRight', False)
    right = [('KeyEvent', 'ArrowUp', True), ('KeyEvent', 'ArrowLeft', False), ('KeyEvent', 'ArrowRight', True)
    forward = [('KeyEvent', 'ArrowUp', True), ('KeyEvent', 'ArrowLeft', False), ('KeyEvent', 'ArrowRight', False)

    #main logic
    while True:
        #increment a counter for number of iterations
        n+=1

        #if atleast one iteration is made, check if turn is needed
        if(n > 1):

            #if atleast one iteration, check if a turn
            if(observation_n[0] != None):
                #store the reward in the previous score
                prev_score = reward_n[0]

                #should we turn?
                if(turn):
                    #pick a random event
                    event = random.choice([left,right])

                    #preform an action
                    action_n = [event for ob in observation_n]

                    #set turn to false
                    turn = False

        elif(~turn):
            #if no turn is needed, go straight
            action_n = [forward for ob in observation_n]

        #if there is an observation, game has started, check if turn is needed
        if(observation_n[0] != None):
            turn, j, total_sum, prev_total_sum = determine_turn(turn, observation_n[0], j, total_sum, prev_total_sum, reward_n[0])

        #save new variables for each iteration
        observation_n, reward_n, done_n, info = env.step(action_n)

        env.render()

if __name__ == '__main__':
    main()