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Eric Liang
52 lines
1.6 KiB
Python
52 lines
1.6 KiB
Python
from math import cos
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from gym.spaces import Box, Tuple, Discrete
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import numpy as np
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from gym.envs.classic_control.mountain_car import MountainCarEnv
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"""
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Multiagent mountain car that sums and then
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averages its actions to produce the velocity
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"""
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class MultiAgentMountainCarEnv(MountainCarEnv):
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def __init__(self):
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self.min_position = -1.2
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self.max_position = 0.6
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self.max_speed = 0.07
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self.goal_position = 0.5
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self.low = np.array([self.min_position, -self.max_speed])
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self.high = np.array([self.max_position, self.max_speed])
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self.viewer = None
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self.action_space = [Discrete(3) for _ in range(2)]
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self.observation_space = Tuple(
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[Box(self.low, self.high, dtype=np.float32) for _ in range(2)])
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self.seed()
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self.reset()
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def step(self, action):
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summed_act = 0.5 * np.sum(action)
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position, velocity = self.state
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velocity += (summed_act - 1) * 0.001
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velocity += cos(3 * position) * (-0.0025)
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velocity = np.clip(velocity, -self.max_speed, self.max_speed)
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position += velocity
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position = np.clip(position, self.min_position, self.max_position)
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if (position == self.min_position and velocity < 0):
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velocity = 0
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done = bool(position >= self.goal_position)
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reward = position
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self.state = (position, velocity)
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return [np.array(self.state) for _ in range(2)], reward, done, {}
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def reset(self):
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self.state = np.array([self.np_random.uniform(low=-0.6, high=-0.4), 0])
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return [np.array(self.state) for _ in range(2)]
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