def test_two_sim_guess_it(self):
gi = TwoPlayerGuessIt()
p = UninformedMCTSPlayer(gi, 2)
init = gi.get_initial_state()
s = p.update_state(init)
s_gt = init
s_gt[0, 0, 0] = 1
s_gt[:, :, -1] = 1
self.assertTrue((s == s_gt).all())
def test_two_sim_guess_it(self):
gi = TwoPlayerGuessIt()
pr = NeuralNetwork(gi, PriorNet)
p = DeepMCTSPlayer(gi, pr, 2)
init = gi.get_initial_state()
s = p.update_state(init)
s_gt = init
s_gt[0, 1, 0] = 1
s_gt[:, :, -1] = 1
self.assertTrue((s == s_gt).all())
def test_six_sim_guess_it(self):
for _ in range(1000): # There should be no stochasticity (temp=0)
gi = TwoPlayerGuessIt()
p = UninformedMCTSPlayer(gi, 6)
init = gi.get_initial_state()
s = p.update_state(init)
s_gt = init
s_gt[-1, -1, 0] = 1
s_gt[:, :, -1] = 1
self.assertTrue((s == s_gt).all())
def test_three_sim_guess_it(self):
for _ in range(100): # There should be no stochasticity (temp=0)
gi = TwoPlayerGuessIt()
pr = NeuralNetwork(gi, PriorNet)
p = DeepMCTSPlayer(gi, pr, 3)
init = gi.get_initial_state()
s = p.update_state(init)
s_gt = init.copy()
s_gt[0, 1, 0] = 1
s_gt[:, :, -1] = 1
self.assertTrue((s == s_gt).all())
def test_reset_guess_it(self):
gi = TwoPlayerGuessIt()
p = UninformedMCTSPlayer(gi, 2)
init = gi.get_initial_state()
s = p.update_state(init)
s_gt = init.copy()
s_gt[0, 0, 0] = 1
s_gt[:, :, -1] = 1
self.assertTrue((s == s_gt).all())
for _ in range(100):
p.reset()
s = p.update_state(init)
self.assertTrue((s == s_gt).all())
def test_reset_guess_it(self):
gi = TwoPlayerGuessIt()
pr = NeuralNetwork(gi, PriorNet)
p = DeepMCTSPlayer(gi, pr, 2)
init = gi.get_initial_state()
s = p.update_state(init)
s_gt = init.copy()
s_gt[0, 1, 0] = 1
s_gt[:, :, -1] = 1
self.assertTrue((s == s_gt).all())
for _ in range(100):
p.reset()
s = p.update_state(init)
self.assertTrue((s == s_gt).all())
def test_dumbnet_predict(self):
gi = TwoPlayerGuessIt()
nn = NeuralNetwork(gi, DumbNet)
init = gi.get_initial_state()
p, v = nn.predict(init)
self.assertEqual(len(p), 4)
self.assertTrue((p == .25).all())
self.assertEqual(v, 0.0)
template = np.zeros_like(gi.get_available_actions(init))
template[0, 1] = 1
s = gi.take_action(init, template)
p, v = nn.predict(s)
self.assertEqual(len(p), 3)
self.assertTrue((p == 0.3333333).all())
self.assertEqual(v, 0.0)
def test_uniform_get_valid_dist(self):
gi = TwoPlayerGuessIt()
nn = NeuralNetwork(gi, DumbNet)
init = gi.get_initial_state()
logits = torch.ones((2, 2))
self.assertEqual(list(nn.get_valid_dist(init, logits)),
[.25, .25, .25, .25])
self.assertEqual(
list(nn.get_valid_dist(init, logits, log_softmax=True)),
[-1.38629436] * 4)
template = np.zeros_like(gi.get_available_actions(init))
template[0, 1] = 1
s = gi.take_action(init, template)
self.assertEqual(list(nn.get_valid_dist(s, logits)), [0.3333333] * 3)
self.assertEqual(list(nn.get_valid_dist(s, logits, log_softmax=True)),
[-1.09861228] * 3)
def test_easy_loss(self):
gi = TwoPlayerGuessIt()
nn = NeuralNetwork(gi, DumbNet)
states = np.array([gi.get_initial_state()])
p_gt = np.ones(4) * .1
p_gt[-1] = .7
p_gt = np.array([p_gt])
p_pred = np.ones((1, 2, 2), dtype=np.float32)
p_pred[0, 1, 1] = 2
p_pred = torch.from_numpy(p_pred)
v_gt, v_pred = torch.Tensor([1.]), torch.Tensor([-.35])
l = nn.loss(states, (p_pred, v_pred), (p_gt, v_gt))
val = l.detach().numpy().reshape(-1)[0]
mse_loss = 1.8225
ce_loss = 1.0437
self.assertAlmostEqual(mse_loss + ce_loss, val, places=4)
def test_prior_get_valid_dist(self):
gi = TwoPlayerGuessIt()
nn = NeuralNetwork(gi, DumbNet)
init = gi.get_initial_state()
logits = torch.ones((2, 2))
logits[:, -1] = 2
dist = [float(x) for x in list(nn.get_valid_dist(init, logits))]
self.assertEqual(dist, [
0.13447073101997375, 0.3655293583869934, 0.13447073101997375,
0.3655293583869934
])
template = np.zeros_like(gi.get_available_actions(init))
template[0, 1] = 1
s = gi.take_action(init, template)
dist = [float(x) for x in list(nn.get_valid_dist(s, logits))]
self.assertEqual(
dist,
[0.21194154024124146, 0.21194154024124146, 0.5761168599128723])
def test_tie_train(self):
gi = TwoPlayerGuessIt()
nn = NeuralNetwork(gi, MLP, weight_decay=0, lr=1e-4)
data = []
s1 = gi.get_initial_state()
data.append([s1, np.array([.3, .2, .1, .4]), .8])
data.append([s1, np.array([.4, .3, .2, .1]), .0])
# Should become [.35, .25, .15, .25], .4
data = np.array(data)
for _ in range(600):
nn.train(data)
p, v = nn.predict(s1)
np.testing.assert_allclose(p,
np.array([.35, .25, .15, .25],
dtype=np.float32),
atol=0.01)
np.testing.assert_allclose(v, .4, atol=.03)
def test_easy_train(self):
gi = TwoPlayerGuessIt()
nn = NeuralNetwork(gi, MLP, weight_decay=0, lr=1e-3)
data = []
s1 = gi.get_initial_state()
s2 = gi.take_action(s1, np.array([[1, 0], [0, 0]]))
s3 = gi.take_action(s2, np.array([[0, 1], [0, 0]]))
data.append([s1, np.array([.1, .1, .1, .7]), .65])
data.append([s2, np.array([.1, .1, .8]), .30])
data.append([s3, np.array([.4, .6]), .1])
data = np.array(data)
for _ in range(350):
nn.train(data)
for row in data:
s = row[0]
p, v = nn.predict(s)
np.testing.assert_allclose(p, row[1].astype(np.float32), atol=1e-5)
np.testing.assert_allclose(v, row[2], atol=1e-5)
def test_combined_loss(self):
gi = TwoPlayerGuessIt()
nn = NeuralNetwork(gi, DumbNet)
init = gi.get_initial_state()
template = np.zeros_like(gi.get_available_actions(init))
template[0, 1] = 1
s = gi.take_action(init, template)
states = np.array([init, s])
p_gt_top = np.ones(4) * .1
p_gt_top[-1] = .7
p_gt_bot = np.ones(3) * .2
p_gt_bot[-1] = .6
p_gt = np.array([p_gt_top, p_gt_bot], dtype=np.object)
p_pred = np.ones((2, 2, 2), dtype=np.float32)
p_pred[:, 1, 1] = 2
p_pred = torch.from_numpy(p_pred)
v_gt, v_pred = torch.Tensor([1., .5]), torch.Tensor([-.35, -.2])
l = nn.loss(states, (p_pred, v_pred),
(p_gt, v_gt)).detach().numpy().reshape(-1)[0]
self.assertAlmostEqual((.49 + .9514) + (1.8225 + 1.0437), l, places=4)
def test_masked_loss(self):
gi = TwoPlayerGuessIt()
nn = NeuralNetwork(gi, DumbNet)
s = gi.get_initial_state()
template = np.zeros_like(gi.get_available_actions(s))
template[0, 1] = 1
s = gi.take_action(s, template)
states = np.array([s])
p_gt = np.ones(3) * .2
p_gt[-1] = .6
p_gt = np.array([p_gt])
p_pred = np.ones((1, 2, 2), dtype=np.float32)
p_pred[0, 1, 1] = 2
p_pred = torch.from_numpy(p_pred)
v_gt, v_pred = torch.Tensor([.5]), torch.Tensor([-.2])
l = nn.loss(states, (p_pred, v_pred),
(p_gt, v_gt)).detach().numpy().reshape(-1)[0]
mse_loss = .49
ce_loss = .9514
self.assertAlmostEqual(mse_loss + ce_loss, l, places=4)
def test_dumbnet_guessit_self_play(self):
gi = TwoPlayerGuessIt()
nn = NeuralNetwork(gi, DumbNet)
t = Trainer(gi, nn, num_simulations=2, num_games=1, num_updates=0, buffer_size_limit=None, cpuct=1, num_threads=1)
data = t.self_play(temperature=0)
np.testing.assert_equal(data[:,-1], np.array([-1, 1, -1, 1]))
s = gi.get_initial_state()
np.testing.assert_equal(data[0,0], s)
np.testing.assert_equal(data[0,1], np.array([1, 0, 0, 0]))
s = gi.take_action(s, np.array([[1,0],[0,0]]))
np.testing.assert_equal(data[1,0], s)
np.testing.assert_equal(data[1,1], np.array([1, 0, 0]))
s = gi.take_action(s, np.array([[0,1],[0,0]]))
np.testing.assert_equal(data[2,0], s)
np.testing.assert_equal(data[2,1], np.array([1, 0]))
s = gi.take_action(s, np.array([[0,0],[1,0]]))
np.testing.assert_equal(data[3,0], s)
np.testing.assert_equal(data[3,1], np.array([1]))
def test_self_play(self):
game = TwoPlayerGuessIt()
p1 = UninformedMCTSPlayer(game, 4)
scores, outcomes = play_match(game, [p1, p1], permute=True)
self.assertEqual(len(scores), 1)
self.assertEqual(len(outcomes), 1)
