You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
70 lines
2.1 KiB
70 lines
2.1 KiB
import gym
|
|
from PIL import Image
|
|
from copy import deepcopy
|
|
import numpy as np
|
|
|
|
from matplotlib import pyplot as plt
|
|
import readchar
|
|
|
|
|
|
env = gym.make("ALE/Skiing-v5", render_mode="human")
|
|
|
|
|
|
observation, info = env.reset()
|
|
y = 40
|
|
|
|
|
|
standstillcounter = 0
|
|
def update_y(y, ski_position):
|
|
global standstillcounter
|
|
if ski_position in [6,7, 8,9]:
|
|
standstillcounter = 0
|
|
y_update = 16
|
|
elif ski_position in [4,5, 10,11]:
|
|
standstillcounter = 0
|
|
y_update = 12
|
|
elif ski_position in [2,3, 12,13]:
|
|
standstillcounter = 0
|
|
y_update = 8
|
|
elif ski_position in [1, 14] and standstillcounter >= 5:
|
|
if standstillcounter >= 8:
|
|
print("!!!!!!!!!! no more x updates!!!!!!!!!!!")
|
|
y_update = 0
|
|
elif ski_position in [1, 14]:
|
|
y_update = 4
|
|
|
|
if ski_position in [1, 14]:
|
|
standstillcounter += 1
|
|
return y_update
|
|
|
|
def update_ski_position(ski_position, action):
|
|
if action == 0:
|
|
return ski_position
|
|
elif action == 1:
|
|
return min(ski_position+1, 14)
|
|
elif action == 2:
|
|
return max(ski_position-1, 1)
|
|
|
|
approx_x_coordinate = 80
|
|
ski_position = 8
|
|
for _ in range(1000000):
|
|
action = env.action_space.sample() # agent policy that uses the observation and info
|
|
action = int(repr(readchar.readchar())[1])
|
|
ski_position = update_ski_position(ski_position, action)
|
|
y_update = update_y(y, ski_position)
|
|
y += y_update if y_update else 0
|
|
|
|
old_x = deepcopy(approx_x_coordinate)
|
|
approx_x_coordinate = int(np.mean(np.where(observation[:,:,1] == 92)[1]))
|
|
print(f"Action: {action},\tski position: {ski_position},\ty_update: {y_update},\ty: {y},\tx: {approx_x_coordinate},\tx_update:{approx_x_coordinate - old_x}")
|
|
observation, reward, terminated, truncated, info = env.step(action)
|
|
if terminated or truncated:
|
|
observation, info = env.reset()
|
|
|
|
|
|
observation, reward, terminated, truncated, info = env.step(0)
|
|
observation, reward, terminated, truncated, info = env.step(0)
|
|
observation, reward, terminated, truncated, info = env.step(0)
|
|
observation, reward, terminated, truncated, info = env.step(0)
|
|
|
|
env.close()
|