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 = rewards.cor2cov(self.R, self.sigma)
self.maze = grid.make_multimaze(4, 4, 3)
self.goals = grid.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 = rewards.cor2cov(R, sigma)
self.assertRaises(sla.LinAlgError, rewards.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 = rewards.cor2cov(R, sigma)
D = rewards.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 = rewards.cor2cov(R, sigma)
D = rewards.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 = rewards.cor2cov(R, sigma)
D = rewards.mvnrewards(numStates, numActions, mu, cov)
self.checkCorrelations(R, D)
# get the target mean vector for rewards. If not given, assume zero means
if not args.rmeans:
args.rmeans = np.zeros(args.tasks)
else:
args.rmeans = np.asarray(ast.literal_eval(args.rmeans))
# read standard deviation for the rewards for each task. If not given, assume
# unit standard deviations
if not args.stdev:
args.stdev = np.ones(args.tasks)
else:
args.stdev = np.asarray(ast.literal_eval(args.stdev))
# compute a covariance matrix from the correlation matrix and standard deviations
cov = rwd.cor2cov(args.correlation, args.stdev)
if not rwd.is_pos_def(cov):
print('Error: covariance matrix must be positive definite', file=sys.stderr)
sys.exit(1)
# maze type instances
if args.type == 'maze':
# TODO: incorporate correlated rewards somehow
maze = grd.make_multimaze(args.rows, args.cols, args.tasks)
goals = grd.maze_goal_states(maze, args.tasks, args.rmeans, cov)
io.write_maze_instance(maze, goals)
print('# type={}, rows={}, cols={}, correlation={}, stdev={}'.
format(args.type, args.rows, args.cols, args.correlation.tolist(), args.stdev.tolist()))