Newer
Older
sequential_decision_environment_1 = GridMDP([[-0.1, -0.1, -0.1, +1],
[-0.1, None, -0.1, -1],
[-0.1, -0.1, -0.1, -0.1]],
terminals=[(3, 2), (3, 1)])
sequential_decision_environment_2 = GridMDP([[-2, -2, -2, +1],
[-2, None, -2, -1],
[-2, -2, -2, -2]],
terminals=[(3, 2), (3, 1)])
sequential_decision_environment_3 = GridMDP([[-1.0, -0.1, -0.1, -0.1, -0.1, 0.5],
[-0.1, None, None, -0.5, -0.1, -0.1],
[-0.1, None, 1.0, 3.0, None, -0.1],
[-0.1, -0.1, -0.1, None, None, -0.1],
[0.5, -0.1, -0.1, -0.1, -0.1, -1.0]],
terminals=[(2, 2), (3, 2), (0, 4), (5, 0)])
assert value_iteration(sequential_decision_environment, .01) == {
(3, 2): 1.0, (3, 1): -1.0,
(3, 0): 0.12958868267972745, (0, 1): 0.39810203830605462,
(0, 2): 0.50928545646220924, (1, 0): 0.25348746162470537,
(0, 0): 0.29543540628363629, (1, 2): 0.64958064617168676,
(2, 0): 0.34461306281476806, (2, 1): 0.48643676237737926,
(2, 2): 0.79536093684710951}
assert value_iteration(sequential_decision_environment_1, .01) == {
(3, 2): 1.0, (3, 1): -1.0,
(3, 0): -0.0897388258468311, (0, 1): 0.146419707398967840,
(0, 2): 0.30596200514385086, (1, 0): 0.010092796415625799,
(0, 0): 0.00633408092008296, (1, 2): 0.507390193380827400,
(2, 0): 0.15072242145212010, (2, 1): 0.358309043654212570,
(2, 2): 0.71675493618997840}
assert value_iteration(sequential_decision_environment_2, .01) == {
(3, 2): 1.0, (3, 1): -1.0,
(3, 0): -3.5141584808407855, (0, 1): -7.8000009574737180,
(0, 2): -6.1064293596058830, (1, 0): -7.1012549580376760,
(0, 0): -8.5872244532783200, (1, 2): -3.9653547121245810,
(2, 0): -5.3099468802901630, (2, 1): -3.3543366255753995,
(2, 2): -1.7383376462930498}
assert value_iteration(sequential_decision_environment_3, .01) == {
(0, 0): 4.350592130345558, (0, 1): 3.640700980321895, (0, 2): 3.0734806370346943, (0, 3): 2.5754335063434937, (0, 4): -1.0,
(1, 0): 3.640700980321895, (1, 1): 3.129579352304856, (1, 4): 2.0787517066719916,
(2, 0): 3.0259220379893352, (2, 1): 2.5926103577982897, (2, 2): 1.0, (2, 4): 2.507774181360808,
(3, 0): 2.5336747364500076, (3, 2): 3.0, (3, 3): 2.292172805400873, (3, 4): 2.996383110867515,
(4, 0): 2.1014575936349886, (4, 3): 3.1297590518608907, (4, 4): 3.6408806798779287,
(5, 0): -1.0, (5, 1): 2.5756132058995282, (5, 2): 3.0736603365907276, (5, 3): 3.6408806798779287, (5, 4): 4.350771829901593}
assert policy_iteration(sequential_decision_environment) == {
(0, 0): (0, 1), (0, 1): (0, 1), (0, 2): (1, 0),
(1, 0): (1, 0), (1, 2): (1, 0), (2, 0): (0, 1),
(2, 1): (0, 1), (2, 2): (1, 0), (3, 0): (-1, 0),
(3, 1): None, (3, 2): None}
assert policy_iteration(sequential_decision_environment_1) == {
(0, 0): (0, 1), (0, 1): (0, 1), (0, 2): (1, 0),
(1, 0): (1, 0), (1, 2): (1, 0), (2, 0): (0, 1),
(2, 1): (0, 1), (2, 2): (1, 0), (3, 0): (-1, 0),
(3, 1): None, (3, 2): None}
assert policy_iteration(sequential_decision_environment_2) == {
(0, 0): (1, 0), (0, 1): (0, 1), (0, 2): (1, 0),
(1, 0): (1, 0), (1, 2): (1, 0), (2, 0): (1, 0),
(2, 1): (1, 0), (2, 2): (1, 0), (3, 0): (0, 1),
(3, 1): None, (3, 2): None}
assert policy_iteration(sequential_decision_environment_3) == {
(0, 0): (-1, 0), (0, 1): (0, -1), (0, 2): (0, -1), (0, 3): (0, -1), (0, 4): None,
(1, 0): (-1, 0), (1, 1): (-1, 0), (1, 4): (1, 0),
(2, 0): (-1, 0), (2, 1): (0, -1), (2, 2): None, (2, 4): (1, 0),
(3, 0): (-1, 0), (3, 2): None, (3, 3): (1, 0), (3, 4): (1, 0),
(4, 0): (-1, 0), (4, 3): (1, 0), (4, 4): (1, 0),
(5, 0): None, (5, 1): (0, 1), (5, 2): (0, 1), (5, 3): (0, 1), (5, 4): (1, 0)}
pi = best_policy(sequential_decision_environment,
value_iteration(sequential_decision_environment, .01))
Surya Teja Cheedella
a validé
assert sequential_decision_environment.to_arrows(pi) == [['>', '>', '>', '.'],
['^', None, '^', '.'],
['^', '>', '^', '<']]
pi_1 = best_policy(sequential_decision_environment_1,
value_iteration(sequential_decision_environment_1, .01))
assert sequential_decision_environment_1.to_arrows(pi_1) == [['>', '>', '>', '.'],
['^', None, '^', '.'],
['^', '>', '^', '<']]
pi_2 = best_policy(sequential_decision_environment_2,
value_iteration(sequential_decision_environment_2, .01))
assert sequential_decision_environment_2.to_arrows(pi_2) == [['>', '>', '>', '.'],
['^', None, '>', '.'],
['>', '>', '>', '^']]
pi_3 = best_policy(sequential_decision_environment_3,
value_iteration(sequential_decision_environment_3, .01))
assert sequential_decision_environment_3.to_arrows(pi_3) == [['.', '>', '>', '>', '>', '>'],
['v', None, None, '>', '>', '^'],
['v', None, '.', '.', None, '^'],
['v', '<', 'v', None, None, '^'],
['<', '<', '<', '<', '<', '.']]
def test_transition_model():
transition_model = {
"A": {"a1": (0.3, "B"), "a2": (0.7, "C")},
"B": {"a1": (0.5, "B"), "a2": (0.5, "A")},
"C": {"a1": (0.9, "A"), "a2": (0.1, "B")},
}
mdp = MDP(init="A", actlist={"a1","a2"}, terminals={"C"}, states={"A","B","C"}, transitions=transition_model)
assert mdp.T("A","a1") == (0.3, "B")
assert mdp.T("B","a2") == (0.5, "A")
assert mdp.T("C","a1") == (0.9, "A")