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