def smoke_test_robot(self):
robot1 = q.Robot()
robot2 = q.Robot()
runner = Runner.from_robots(robot1, robot2)
#runner.settings.max_turns = 5
scores = runner.run()
assert type(scores) is list
def test_runner_from_robots(self):
print(TestRobot().__class__)
runner = Runner.from_robots([TestRobot(), TestRobot()])
self.assertEqual(runner.options, Options())
runner.settings.max_turns = 1
runner.options.quiet = 4
runner.run()
def test_runner_from_robots(self):
print(TestRobot().__class__)
runner = Runner.from_robots([TestRobot(), TestRobot()])
self.assertEqual(runner.options, Options())
runner.settings.max_turns = 1
runner.options.quiet = 4
runner.run()
def test_robot(self):
training_robot = Robot()
robot2 = Robot()
delta = training_robot.delta_callback
runner = Runner.from_robots(training_robot, robot2,
delta_callback=delta)
runner.run()
self.assertNotEqual(training_robot.qlearning.q, robot2.qlearning.q)
def test_runner_delta_callback(self):
def callback(delta, game_state):
global called
called = True
assert type(delta) is list
assert type(delta[0]) is rgkit.settings.AttrDict
assert type(game_state) is gamestate.GameState
runner = Runner.from_robots(TestRobot(), TestRobot(),
delta_callback=callback)
runner.run()
assert called
def test_runner_delta_callback(self):
def callback(delta, game_state):
global called
called = True
assert type(delta) is list
assert type(delta[0]) is rgkit.settings.AttrDict
assert type(game_state) is gamestate.GameState
runner = Runner.from_robots([TestRobot(), TestRobot()],
delta_callback=callback)
runner.options.quiet = 4
runner.run()
assert called
def setUp(self):
# needed to initialize global settings
Runner.from_robots(Robot(), Robot())
def test_runner_from_robots_perf(self):
runner = Runner.from_robots([TestRobot(), TestRobot()])
runner.options.n_of_games = 100
runner.settings.max_turns = 100
def test_runner_from_robots_perf(self):
runner = Runner.from_robots([TestRobot(), TestRobot()])
runner.options.n_of_games = 100
runner.settings.max_turns = 100
def reward(my_delta):
damage_taken = my_delta.hp - my_delta.hp_end
reward = my_delta.damage_caused - damage_taken
return reward
def delta_callback(self, delta, actions, new_gamestate):
future_game = new_gamestate.get_game_info(self.player_id)
print "delta_callback calle"
print("Size of Q: " + str(len(self.qlearning.q.hash_matrix)))
for (robot_loc, robot) in self.game.robots.items():
if hasattr(robot, 'robot_id') and robot.robot_id in self.robot_ids:
action = self.last_action[robot.robot_id]
for delta_me in delta:
state_template = State.from_game(future_game,
self.player_id)
if delta_me['loc'] == robot_loc:
future_state = copy.deepcopy(state_template)
future_state.robot_loc = delta_me.loc_end
reward = self.reward(delta_me)
self.qlearning.learn(self.current_state, future_state,
action, reward)
if __name__ == "__main__":
training_robot = Robot()
robot2 = Robot()
runner = Runner.from_robots(training_robot, robot2,
delta_callback=training_robot.delta_callback)
runner.run()
