Added balancing reacher task and stochastic search task interface

alr_envs/stochastic_search/__init__.py

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+from alr_envs.stochastic_search.stochastic_search import StochasticSearchEnv

alr_envs/stochastic_search/functions/f_base.py

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+import numpy as np
+import scipy.stats as scistats
+
+np.seterr(divide='ignore', invalid='ignore')
+
+
+class BaseObjective(object):
+    def __init__(self, dim, int_opt=None, val_opt=None, alpha=None, beta=None):
+        self.dim = dim
+        self.alpha = alpha
+        self.beta = beta
+        # check if optimal parameter is in interval...
+        if int_opt is not None:
+            self.x_opt = np.random.uniform(int_opt[0], int_opt[1], size=(1, dim))
+        # ... or based on a single value
+        elif val_opt is not None:
+            self.one_pm = np.where(np.random.rand(1, dim) > 0.5, 1, -1)
+            self.x_opt = val_opt * self.one_pm
+        else:
+            raise ValueError("Optimal value or interval has to be defined")
+        self.f_opt = np.round(np.clip(scistats.cauchy.rvs(loc=0, scale=100, size=1)[0], -1000, 1000), decimals=2)
+        self.i = np.arange(self.dim)
+        self._lambda_alpha = None
+        self._q = None
+        self._r = None
+
+    def __call__(self, x):
+        return self.evaluate_full(x)
+
+    def evaluate_full(self, x):
+        raise NotImplementedError("Subclasses should implement this!")
+
+    def gs(self):
+        # Gram Schmidt ortho-normalization
+        a = np.random.randn(self.dim, self.dim)
+        b, _ = np.linalg.qr(a)
+        return b
+
+    # TODO: property probably unnecessary
+    @property
+    def q(self):
+        if self._q is None:
+            self._q = self.gs()
+        return self._q
+
+    @property
+    def r(self):
+        if self._r is None:
+            self._r = self.gs()
+        return self._r
+
+    @property
+    def lambda_alpha(self):
+        if self._lambda_alpha is None:
+            if isinstance(self.alpha, int):
+                lambda_ii = np.power(self.alpha, 1 / 2 * self.i / (self.dim - 1))
+                self._lambda_alpha = np.diag(lambda_ii)
+            else:
+                lambda_ii = np.power(self.alpha[:, None], 1 / 2 * self.i[None, :] / (self.dim - 1))
+                self._lambda_alpha = np.stack([np.diag(l_ii) for l_ii in lambda_ii])
+        return self._lambda_alpha
+
+    @staticmethod
+    def f_pen(x):
+        return np.sum(np.maximum(0, np.abs(x) - 5), axis=1)
+
+    def t_asy_beta(self, x):
+        # exp = np.power(x, 1 + self.beta * self.i[:, None] / (self.input_dim - 1) * np.sqrt(x))
+        # return np.where(x > 0, exp, x)
+        return x
+
+    def t_osz(self, x):
+        x_hat = np.where(x != 0, np.log(np.abs(x)), 0)
+        c_1 = np.where(x > 0, 10, 5.5)
+        c_2 = np.where(x > 0, 7.9, 3.1)
+        return np.sign(x) * np.exp(x_hat + 0.049 * (np.sin(c_1 * x_hat) + np.sin(c_2 * x_hat)))

alr_envs/stochastic_search/functions/f_rosenbrock.py

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+import numpy as np
+
+from alr_envs.stochastic_search.functions.f_base import BaseObjective
+
+
+class Rosenbrock(BaseObjective):
+    def __init__(self, dim, int_opt=(-3., 3.)):
+        super(Rosenbrock, self).__init__(dim, int_opt=int_opt)
+        self.c = np.maximum(1, np.sqrt(self.dim) / 8)
+
+    def evaluate_full(self, x):
+        x = np.atleast_2d(x)
+        assert x.shape[1] == self.dim
+
+        z = self.c * (x - self.x_opt) + 1
+        z_end = z[:, 1:]
+        z_begin = z[:, :-1]
+
+        a = z_begin ** 2 - z_end
+        b = z_begin - 1
+
+        return np.sum(100 * a ** 2 + b ** 2, axis=1) + self.f_opt
+
+
+class RosenbrockRotated(BaseObjective):
+    def __init__(self, dim, int_opt=(-3., 3.)):
+        super(RosenbrockRotated, self).__init__(dim, int_opt=int_opt)
+        self.c = np.maximum(1, np.sqrt(self.dim) / 8)
+
+    def evaluate_full(self, x):
+        x = np.atleast_2d(x)
+        assert x.shape[1] == self.dim
+
+        z = (self.c * self.r @ x.T + 1 / 2).T
+        a = z[:, :-1] ** 2 - z[:, 1:]
+        b = z[:, :-1] - 1
+
+        return np.sum(100 * a ** 2 + b ** 2, axis=1) + self.f_opt
+
+
+class RosenbrockRaw(BaseObjective):
+    def __init__(self, dim, int_opt=(-3., 3.)):
+        super(RosenbrockRaw, self).__init__(dim, int_opt=int_opt)
+        self.x_opt = np.ones((1, dim))
+        self.f_opt = 0
+
+    def evaluate_full(self, x):
+        x = np.atleast_2d(x)
+        assert x.shape[1] == self.dim
+
+        a = x[:, :-1] ** 2 - x[:, 1:]
+        b = x[:, :-1] - 1
+
+        out = np.sum(100 * a ** 2 + b ** 2, axis=1)
+
+        return out

alr_envs/stochastic_search/stochastic_search.py

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