23 lines
676 B
Python
23 lines
676 B
Python
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import gym
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import numpy as np
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from alr_envs.stochastic_search.functions.f_base import BaseObjective
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class StochasticSearchEnv(gym.Env):
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def __init__(self, cost_f: BaseObjective):
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self.cost_f = cost_f
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self.action_space = gym.spaces.Box(low=-np.inf, high=np.inf, shape=(self.cost_f.dim,), dtype=np.float64)
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self.observation_space = gym.spaces.Box(low=(), high=(), shape=(), dtype=np.float64)
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def step(self, action):
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return np.zeros(self.observation_space.shape), np.squeeze(-self.cost_f(action)), True, {}
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def reset(self):
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return np.zeros(self.observation_space.shape)
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def render(self, mode='human'):
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pass
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