from time import sleep
import school_env_builder
from random import randint
class FoolishStudent:
def __init__(self, agent_name):
self.agent_name = agent_name
self.env = school_env_builder.build()
def study(self, epochs):
for _ in range(epochs):
state = self.env.reset()
while True:
action = randint(0, self.env.action_space.size()-1)
next_state, reward, done, _ = self.env.step(action)
if reward < 0:
print('You are so stupid')
if done:
break
state = next_state
def take_a_break(self, epochs):
for _ in range(epochs):
sleep(1)
if __name__ == '__main__':
sl = FoolishStudent('songlei')
for day in range(365):
sl.study(0)
sl.take_a_break(10000000000)
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lamda-bbo/MCTS-VS
lamda-bbo/MCTS-VS PublicOfficial implementation of NeurIPS'22 paper "Monte Carlo Tree Search based Variable Selection for High-Dimensional Bayesian Optimization"
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