70 lines
2.1 KiB
70 lines
2.1 KiB
import gym
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from PIL import Image
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from copy import deepcopy
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import numpy as np
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from matplotlib import pyplot as plt
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import readchar
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env = gym.make("ALE/Skiing-v5", render_mode="human")
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observation, info = env.reset()
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y = 40
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standstillcounter = 0
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def update_y(y, ski_position):
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global standstillcounter
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if ski_position in [6,7, 8,9]:
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standstillcounter = 0
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y_update = 16
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elif ski_position in [4,5, 10,11]:
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standstillcounter = 0
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y_update = 12
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elif ski_position in [2,3, 12,13]:
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standstillcounter = 0
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y_update = 8
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elif ski_position in [1, 14] and standstillcounter >= 5:
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if standstillcounter >= 8:
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print("!!!!!!!!!! no more x updates!!!!!!!!!!!")
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y_update = 0
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elif ski_position in [1, 14]:
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y_update = 4
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if ski_position in [1, 14]:
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standstillcounter += 1
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return y_update
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def update_ski_position(ski_position, action):
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if action == 0:
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return ski_position
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elif action == 1:
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return min(ski_position+1, 14)
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elif action == 2:
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return max(ski_position-1, 1)
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approx_x_coordinate = 80
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ski_position = 8
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for _ in range(1000000):
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action = env.action_space.sample() # agent policy that uses the observation and info
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action = int(repr(readchar.readchar())[1])
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ski_position = update_ski_position(ski_position, action)
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y_update = update_y(y, ski_position)
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y += y_update if y_update else 0
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old_x = deepcopy(approx_x_coordinate)
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approx_x_coordinate = int(np.mean(np.where(observation[:,:,1] == 92)[1]))
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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}")
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observation, reward, terminated, truncated, info = env.step(action)
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if terminated or truncated:
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observation, info = env.reset()
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observation, reward, terminated, truncated, info = env.step(0)
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observation, reward, terminated, truncated, info = env.step(0)
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observation, reward, terminated, truncated, info = env.step(0)
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observation, reward, terminated, truncated, info = env.step(0)
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env.close()
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