35 lines
1.3 KiB
Python
35 lines
1.3 KiB
Python
import numpy as np
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from mcts import MCTS
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import matplotlib.pyplot as plt
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class TestEnv:
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def __init__(self, max_step=5):
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self.max_step = max_step
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self.reward = {i:np.random.uniform() for i in range(2**max_step)}
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self.best = max(self.reward.items(), key=lambda x:x[1])
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print("The best arm is {} with expected reward {}".format(self.best[0],self.best[1]))
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def step_forward(self, state, action):
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if action != 0 and action != 1:
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raise ValueError("Action must be 0 or 1!")
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if state[0] >= 2**state[1] or state[1] >= self.max_step:
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raise ValueError("Invalid State!")
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# print("Operate action {} at state {}, timestep {}".format(action, state[0], state[1]))
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state[0] = state[0] + 2**state[1]*action
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state[1] = state[1] + 1
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return state
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def evaluator(self, state):
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if state[1] == self.max_step:
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reward = int(np.random.uniform() > self.reward[state[0]])
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is_terminated = True
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else:
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reward = 0
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is_terminated = False
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return reward, is_terminated
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if __name__=="__main__":
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env = TestEnv(1)
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evaluator = lambda state: env.evaluator(state)
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mcts = MCTS(env, evaluator, [0,0], 2, np.ones([2])/2, max_step=1e4)
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