q-table python 2 actions and 2 states rienforcements code example

Example: q learning algorithm

if start_q_table is None:
    # initialize the q-table#
    q_table = {}
    for i in range(-SIZE+1, SIZE):
        for ii in range(-SIZE+1, SIZE):
            for iii in range(-SIZE+1, SIZE):
                    for iiii in range(-SIZE+1, SIZE):
                        q_table[((i, ii), (iii, iiii))] = [np.random.uniform(-5, 0) for i in range(4)]

else:
    with open(start_q_table, "rb") as f:
        q_table = pickle.load(f)


# can look up from Q-table with: print(q_table[((-9, -2), (3, 9))]) for example

episode_rewards = []

for episode in range(HM_EPISODES):
    player = Blob()
    food = Blob()
    enemy = Blob()
    if episode % SHOW_EVERY == 0:
        print(f"on #{episode}, epsilon is {epsilon}")
        print(f"{SHOW_EVERY} ep mean: {np.mean(episode_rewards[-SHOW_EVERY:])}")
        show = True
    else:
        show = False

    episode_reward = 0
    for i in range(200):
        obs = (player-food, player-enemy)
        #print(obs)
        if np.random.random() > epsilon:
            # GET THE ACTION
            action = np.argmax(q_table[obs])
        else:
            action = np.random.randint(0, 4)
        # Take the action!
        player.action(action)

        #### MAYBE ###
        #enemy.move()
        #food.move()
        ##############

        if player.x == enemy.x and player.y == enemy.y:
            reward = -ENEMY_PENALTY
        elif player.x == food.x and player.y == food.y:
            reward = FOOD_REWARD
        else:
            reward = -MOVE_PENALTY

        new_obs = (player-food, player-enemy)
        max_future_q = np.max(q_table[new_obs])
        current_q = q_table[obs][action]

        if reward == FOOD_REWARD:
            new_q = FOOD_REWARD
        else:
            new_q = (1 - LEARNING_RATE) * current_q + LEARNING_RATE * (reward + DISCOUNT * max_future_q)
        q_table[obs][action] = new_q

        if show:
            env = np.zeros((SIZE, SIZE, 3), dtype=np.uint8)  # starts an rbg of our size
            env[food.x][food.y] = d[FOOD_N]  # sets the food location tile to green color
            env[player.x][player.y] = d[PLAYER_N]  # sets the player tile to blue
            env[enemy.x][enemy.y] = d[ENEMY_N]  # sets the enemy location to red
            img = Image.fromarray(env, 'RGB')
            img = img.resize((300, 300))
            cv2.imshow("image", np.array(img))
            if reward == FOOD_REWARD or reward == -ENEMY_PENALTY:
                if cv2.waitKey(500) & 0xFF == ord('q'):
                    break
            else:
                if cv2.waitKey(1) & 0xFF == ord('q'):
                    break

        episode_reward += reward
        if reward == FOOD_REWARD or reward == -ENEMY_PENALTY:
            break