def setUp(self):
self.R = np.asarray([[1.0, 0.4, -0.4], [0.4, 1.0, 0.6],
[-0.4, 0.6, 1.0]])
self.mu = [100.0] * 3
self.sigma = [10.0] * 3
self.cov = gen.cor2cov(self.R, self.sigma)
self.maze = gen.make_multimaze(4, 4, 3)
self.goals = gen.maze_goal_states(self.maze, 3, self.mu, self.cov)
def setUp(self):
self.R = np.asarray([[ 1.0, 0.4, -0.4],
[ 0.4, 1.0, 0.6],
[-0.4, 0.6, 1.0]])
self.mu = [100.0] * 3
self.sigma = [10.0] * 3
self.cov = gen.cor2cov(self.R, self.sigma)
self.maze = gen.make_multimaze(4, 4, 3)
self.goals = gen.maze_goal_states(self.maze, 3, self.mu, self.cov)
def test_invalidR(self):
numStates = 1000
numActions = 10
mu = [0.0] * 3
sigma = [1.0] * 3
R = np.asarray([[1.0, -0.7, 0.8], [-0.7, 1.0, 0.9], [0.8, 0.9, 1.0]])
cov = gen.cor2cov(R, sigma)
self.assertRaises(sla.LinAlgError, gen.mvnrewards, numStates,
numActions, mu, R)
def test_rewards2(self):
numStates = 5000
numActions = 20
mu = [10, 10, 10]
sigma = [1, 1, 1]
R = np.asarray([[1.0, -0.7, -0.5], [-0.7, 1.0, 0.8], [-0.5, 0.8, 1.0]])
cov = gen.cor2cov(R, sigma)
D = gen.mvnrewards(numStates, numActions, mu, cov)
self.checkCorrelations(R, D)
def test_rewards1(self):
numStates = 1000
numActions = 5
mu = [20, 0, 50]
sigma = [5, 5, 10]
R = np.asarray([[1.0, 0.4, -0.4], [0.4, 1.0, 0.6], [-0.4, 0.6, 1.0]])
cov = gen.cor2cov(R, sigma)
D = gen.mvnrewards(numStates, numActions, mu, cov)
self.checkCorrelations(R, D)
def test_invalidR(self):
numStates = 1000
numActions = 10
mu = [0.0] * 3
sigma = [1.0] * 3
R = np.asarray([[ 1.0, -0.7, 0.8],
[-0.7, 1.0, 0.9],
[ 0.8, 0.9, 1.0]])
cov = gen.cor2cov(R, sigma)
self.assertRaises(sla.LinAlgError, gen.mvnrewards, numStates, numActions, mu, R)
def test_rewards3(self):
numStates = 200
numActions = 8
mu = [0, -10, 30, 0]
sigma = [5, 0.5, 10, 2.0]
R = np.asarray([[1.0, 0.2, -0.5, 0.0], [0.2, 1.0, 0.4, 0.0],
[-0.5, 0.4, 1.0, 0.6], [0.0, 0.0, 0.6, 1.0]])
cov = gen.cor2cov(R, sigma)
D = gen.mvnrewards(numStates, numActions, mu, cov)
self.checkCorrelations(R, D)
def test_rewards2(self):
numStates = 5000
numActions = 20
mu = [10, 10, 10]
sigma = [1, 1, 1]
R = np.asarray([[ 1.0, -0.7, -0.5],
[-0.7, 1.0, 0.8],
[-0.5, 0.8, 1.0]])
cov = gen.cor2cov(R, sigma)
D = gen.mvnrewards(numStates, numActions, mu, cov)
self.checkCorrelations(R, D)
def test_rewards1(self):
numStates = 1000
numActions = 5
mu = [20, 0, 50]
sigma = [5, 5, 10]
R = np.asarray([[ 1.0, 0.4, -0.4],
[ 0.4, 1.0, 0.6],
[-0.4, 0.6, 1.0]])
cov = gen.cor2cov(R, sigma)
D = gen.mvnrewards(numStates, numActions, mu, cov)
self.checkCorrelations(R, D)
def test_rewards3(self):
numStates = 200
numActions = 8
mu = [0, -10, 30, 0]
sigma = [5, 0.5, 10, 2.0]
R = np.asarray([[ 1.0, 0.2, -0.5, 0.0],
[ 0.2, 1.0, 0.4, 0.0],
[-0.5, 0.4, 1.0, 0.6],
[ 0.0, 0.0, 0.6, 1.0]])
cov = gen.cor2cov(R, sigma)
D = gen.mvnrewards(numStates, numActions, mu, cov)
self.checkCorrelations(R, D)