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https://github.com/zhm-real/PathPlanning.git
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zhm-real
88 lines
2.1 KiB
Python
88 lines
2.1 KiB
Python
class Env:
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def __init__(self, xI, xG):
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self.x_range = 51 # size of background
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self.y_range = 31
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self.motions = [(1, 0), (-1, 0), (0, 1), (0, -1)]
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self.xI = xI
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self.xG = xG
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self.obs = self.obs_map()
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self.lose = self.lose_map()
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self.stateSpace = self.state_space()
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def obs_map(self):
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"""
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Initialize obstacles' positions
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:return: map of obstacles
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"""
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x = self.x_range
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y = self.y_range
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obs = []
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for i in range(x):
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obs.append((i, 0))
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for i in range(x):
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obs.append((i, y - 1))
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for i in range(y):
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obs.append((0, i))
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for i in range(y):
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obs.append((x - 1, i))
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for i in range(10, 21):
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obs.append((i, 15))
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for i in range(15):
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obs.append((20, i))
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for i in range(15, 30):
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obs.append((30, i))
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for i in range(16):
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obs.append((40, i))
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return obs
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def lose_map(self):
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"""
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Initialize losing states' positions
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:return: losing states
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"""
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lose = []
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for i in range(25, 36):
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lose.append((i, 13))
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return lose
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def state_space(self):
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"""
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generate state space
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:return: state space
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"""
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state_space = []
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for i in range(self.x_range):
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for j in range(self.y_range):
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if (i, j) not in self.obs:
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state_space.append((i, j))
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return state_space
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def get_reward(self, x_next):
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"""
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calculate reward of next state
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:param x_next: next state
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:return: reward
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"""
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reward = []
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for x in x_next:
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if x in self.xG:
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reward.append(10) # reward : 10, for goal states
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elif x in self.lose:
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reward.append(-10) # reward : -10, for lose states
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else:
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reward.append(0) # reward : 0, for other states
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return reward
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