def run_match(bot1, bot2):
#rgkit integration
runner = Runner(player_files=(bot1,bot2), options=Options(quiet=4, game_seed=random.randint(0, default_settings.max_seed)))
scores0, scores1 = runner.run()[0]
if scores0 > scores1:
return (scores0, scores1, scores0 - scores1, bot1)
elif scores1 > scores0:
return (scores0, scores1, scores1 - scores0, bot2)
else:
return (scores0, scores1, 0,'tie')
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())
#!/usr/bin/python from evolution_bot import ScoredPerceptron, Robot, Collective from rgkit.run import Runner, Options from rgkit.game import Player snapshots = [1000, 2000, 3000, 4000, 5000] for t in snapshots: loadfile0 = "evolved_brains/sim0/red/perceptron_100_25_10_turn%d_gen0.npz" % (t - 1) loadfile1 = "evolved_brains/sim0/blue/perceptron_100_25_10_turn%d_gen0.npz" % (t - 1) try: brain0 = ScoredPerceptron.load(loadfile0) pop0 = Collective(1, 0, 0, [brain0]) robot0 = Robot(pop0) player0 = Player(name="red %d" % t, robot=robot0) brain1 = ScoredPerceptron.load(loadfile1) pop1 = Collective(1, 0, 0, [brain1]) robot1 = Robot(pop1) player1 = Player(name="blu %d" % t, robot=robot1) opts = Options(print_info=True) r = Runner(players=[player0, player1], options=opts) r.run() except: print "failed to run turn %d" % t
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_default_settings(self):
settings = Runner.default_settings()
assert settings is not None
assert settings == rgkit_settings
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_default_settings(self):
settings = Runner.default_settings()
assert settings is not None
assert settings == rgkit_settings
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()
def run(self, number_of_games=1):
runner = Runner(player1_file=self.player_file,
player2_file=self.opponent_file,
delta_callback=self._delta_callback)
runner.options.n_of_games = number_of_games
runner.run()