def __init__(self, ctx, generator, driver):
'''Creates a new game in world where driver will interact with the game.'''
self._context = ctx
self._sim = simulation.Simulation(generator)
self._driver = driver
self._wins = 0
self._losses = 0
self._was_in_terminal_state = False
def testTrain(self):
g = tf.Graph()
net = pg.PolicyGradientNetwork('testTrain', g, (4, 4))
s = tf.Session(graph=g)
with g.as_default():
init = tf.global_variables_initializer()
s.run(init)
sim = simulation.Simulation(world.Generator(4, 4))
state = sim.to_array()
net.train(s, [[(state, 3, 7), (state, 3, -1)], [(state, 0, 1000)]])
def test_interact(self):
TEST_ACTION = movement.ACTION_RIGHT
q = grid.QTable(-1)
q.set((0, 0), TEST_ACTION, 1)
player = grid.MachinePlayer(grid.GreedyQ(q), grid.StubLearner())
w = world.World.parse('@.')
with patch.object(simulation.Simulation, 'act') as mock_act:
sim = simulation.Simulation(world.Static(w))
ctx = context.StubContext()
player.interact(ctx, sim)
mock_act.assert_called_once_with(TEST_ACTION)
def testPredict(self):
g = tf.Graph()
net = pg.PolicyGradientNetwork('testPredict', g, (7, 11))
s = tf.Session(graph=g)
with g.as_default():
init = tf.global_variables_initializer()
s.run(init)
sim = simulation.Simulation(world.Generator(11, 7))
state = sim.to_array()
[[act], _] = net.predict(s, [state])
self.assertTrue(0 <= act)
self.assertTrue(act < len(movement.ALL_ACTIONS))
def testUpdate_lossDecreases(self):
w = world.World.parse('@.....$')
g = tf.Graph()
net = pg.PolicyGradientNetwork('testUpdate_lossDecreases', g, (w.h, w.w))
s = tf.Session(graph=g)
with g.as_default():
init = tf.global_variables_initializer()
s.run(init)
state = simulation.Simulation(world.Static(w)).to_array()
losses = []
for _ in range(10):
loss, _ = s.run([net.loss, net.update], feed_dict={
net.state: [state],
net.action_in: [[1]],
net.advantage: [[2]]
})
losses.append(loss)
self.assertTrue(losses[-1] < losses[0])
def test_to_array(self):
w = world.World.parse('$.@^#')
sim = simulation.Simulation(world.Static(w))
self.assertTrue(
(np.array([[2, 3, 4, 5, 1]], dtype=np.int8) == sim.to_array())
.all())
def test_act_accumulates_score(self):
w = world.World.parse('@.')
sim = simulation.Simulation(world.Static(w))
sim.act(movement.ACTION_RIGHT)
sim.act(movement.ACTION_LEFT)
self.assertEqual(-2, sim.score)
def test_in_terminal_state(self):
w = world.World.parse('@^')
sim = simulation.Simulation(world.Static(w))
self.assertFalse(sim.in_terminal_state)
sim.act(movement.ACTION_RIGHT)
self.assertTrue(sim.in_terminal_state)