def __init__(self, game, controller=None, **kwargs):
GameEnvironment.__init__(self, game, visualize=True, **kwargs)
self.controller = controller
self.screen = SubjectiveSceen()
self.screen._initScreen()
self.reset()
self.state = self.getState()
self.eventHistory = list()
def performAction(self, ACTION=None, action=None):
if ACTION is None:
self.setState(self.state)
GameEnvironment.performAction(self, action)
if action is not None:
self._drawState()
pygame.time.wait(self.actionDelay)
self.eventHistory.append(action)
self.state = self.getState()
else:
GameEnvironment.performAction(self, ACTION)
def test2():
from examples.gridphysics.mazes import polarmaze_game, maze_level_1
from vgdl.core import VGDLParser
game_str, map_str = polarmaze_game, maze_level_1
g = VGDLParser().parseGame(game_str)
g.buildLevel(map_str)
actions = [1, 0, 0, 3, 0, 2, 0, 2, 0, 0, 0]
env = GameEnvironment(g, visualize=True, actionDelay=100)
env.rollOut(actions)
env.reset()
senv = SubjectiveGame(g, actionDelay=1500)
senv.rollOut(actions)
def test6():
""" Now with memory!"""
from numpy import ndarray
from examples.gridphysics.mazes import polarmaze_game
from pybrain.optimization import SNES
g = VGDLParser().parseGame(polarmaze_game)
g.buildLevel(cheese_maze)
game_env = GameEnvironment(g)
net = buildNet(game_env.outdim, 10, 4, temperature=0.1, recurrent=True)
algo = SNES(lambda x: someEpisodes(game_env, x, avgOver=6, maxSteps=30, exploretoo=False), net, verbose=True, desiredEvaluation=0.85)
print algo.batchSize
rows, cols = 2,3
episodesPerStep = 5
for i in range(rows*cols):
pylab.subplot(rows, cols, i+1)
algo.learn(episodesPerStep)
if isinstance(algo.bestEvaluable, ndarray):
net._setParameters(algo.bestEvaluable)
else:
net = algo.bestEvaluable
plotBackground(game_env)
plotTrajectories(game_env, net)
pylab.title(str((i+1)*episodesPerStep))
if algo.desiredEvaluation <= algo.bestEvaluation:
break
print
pylab.show()
def test4():
from numpy import ndarray
from examples.gridphysics.mazes import polarmaze_game
from pybrain.optimization import SNES, WeightGuessing
g = VGDLParser().parseGame(polarmaze_game)
g.buildLevel(labyrinth2)
game_env = GameEnvironment(g)
net = buildNet(game_env.outdim, 5, 4, temperature=0.1, recurrent=False)
algo = SNES(lambda x: someEpisodes(game_env, x, avgOver=3), net, verbose=True, desiredEvaluation=0.75)
#algo = WeightGuessing(lambda x: someEpisodes(game_env, x), net, verbose=True, desiredEvaluation=0.78)
rows, cols = 2,2
episodesPerStep = 4
for i in range(rows*cols):
pylab.subplot(rows, cols, i+1)
algo.learn(episodesPerStep)
if isinstance(algo.bestEvaluable, ndarray):
net._setParameters(algo.bestEvaluable)
else:
net = algo.bestEvaluable
plotBackground(game_env)
plotTrajectories(game_env, net)
pylab.title(str((i+1)*episodesPerStep))
if algo.desiredEvaluation <= algo.bestEvaluation:
break
print
pylab.show()
def test3():
from examples.gridphysics.mazes.simple import consistent_corridor
from examples.gridphysics.mazes import polarmaze_game
from pybrain.optimization import SNES
g = VGDLParser().parseGame(polarmaze_game)
g.buildLevel(consistent_corridor)
game_env = GameEnvironment(g)
net = buildNet(game_env.outdim, 4, 4, temperature=0.05, recurrent=False)
algo = SNES(lambda x: someEpisodes(game_env, x),
net,
verbose=True,
desiredEvaluation=0.78)
rows, cols = 2, 2
episodesPerStep = 3
for i in range(rows * cols):
pylab.subplot(rows, cols, i + 1)
algo.learn(episodesPerStep)
net._setParameters(algo.bestEvaluable)
plotBackground(game_env)
plotTrajectories(game_env, net)
pylab.title(str((i + 1) * episodesPerStep))
if algo.desiredEvaluation <= algo.bestEvaluation:
break
print
pylab.show()
def test2():
from examples.gridphysics.mazes import polarmaze_game, maze_level_1
from pybrain.optimization import SNES
game_str, map_str = polarmaze_game, maze_level_1
g = VGDLParser().parseGame(game_str)
g.buildLevel(map_str)
game_env = GameEnvironment(g, actionDelay=100, recordingEnabled=True)
net = buildNet(game_env.outdim, 6, 2)
algo = SNES(lambda x: someEpisodes(game_env, x),
net,
verbose=True,
desiredEvaluation=0.43)
rows, cols = 3, 3
episodesPerStep = 2
for i in range(rows * cols):
pylab.subplot(rows, cols, i + 1)
algo.learn(episodesPerStep)
net._setParameters(algo.bestEvaluable)
plotBackground(game_env)
plotTrajectories(game_env, net)
pylab.title(str((i + 1) * episodesPerStep))
if algo.desiredEvaluation <= algo.bestEvaluation:
break
print
pylab.show()
def test4():
""" Same thing, but animated. """
from examples.gridphysics.mazes.windy import windy_stoch_game, windy_level
from pybrain.rl.experiments.episodic import EpisodicExperiment
from vgdl.interfaces import GameEnvironment, GameTask
from vgdl.agents import PolicyDrivenAgent
g = VGDLParser().parseGame(windy_stoch_game)
g.buildLevel(windy_level)
env = GameEnvironment(g, visualize=True, actionDelay=100)
task = GameTask(env)
agent = PolicyDrivenAgent.buildOptimal(env)
exper = EpisodicExperiment(task, agent)
res = exper.doEpisodes(5)
print res
def __init__(self,
game=None,
verbose=False,
actionset=BASEDIRS,
env=None,
avgOver=10):
if env is None:
env = GameEnvironment(game, actionset=actionset)
self.env = env
self.verbose = verbose
self.sas_tuples = []
self.rewards = {}
if env._game.is_stochastic:
# in the stochastic case, how often is every state-action pair tried?
self.avgOver = avgOver
else:
self.avgOver = 1
def test1():
from examples.gridphysics.mazes import polarmaze_game, maze_level_1
game_str, map_str = polarmaze_game, maze_level_1
g = VGDLParser().parseGame(game_str)
g.buildLevel(map_str)
game_env = GameEnvironment(g)
print 'number of observations:', game_env.outdim
net = buildNet(game_env.outdim, 2, 2)
for i in range(200):
net.randomize()
net.reset()
print someEpisodes(game_env, net),
if i% 20 == 19:
print
def test2():
from examples.gridphysics.mazes import polarmaze_game, maze_level_1
from vgdl.core import VGDLParser
game_str, map_str = polarmaze_game, maze_level_1
g = VGDLParser().parseGame(game_str)
g.buildLevel(map_str)
actions = [1, 0, 0, 3, 0, 2, 0, 2, 0, 0, 0]
env = GameEnvironment(g, visualize=True, actionDelay=100)
env.rollOut(actions)
env.reset()
senv = SubjectiveGame(g, actionDelay=1500)
senv.rollOut(actions)
def __init__(self, game, **kwargs):
GameEnvironment.__init__(self, game, visualize=False, **kwargs)
self.screen = SubjectiveSceen()
self.screen._initScreen()
assert self.orientedAvatar, 'Only oriented/directional avatars are currently supported for the first-person view.'
self.reset()
def performAction(self, action):
GameEnvironment.performAction(self, action)
if action is not None:
self._drawState()
pygame.time.wait(self.actionDelay)
def reset(self):
GameEnvironment.reset(self)
if hasattr(self, 'screen'):
self.screen.reset()
pygame.display.flip()
def __init__(self, game, **kwargs):
GameEnvironment.__init__(self, game, visualize=False, **kwargs)
self.screen = SubjectiveSceen()
self.screen._initScreen()
assert self.orientedAvatar, 'Only oriented/directional avatars are currently supported for the first-person view.'
self.reset()
def performAction(self, action):
GameEnvironment.performAction(self, action)
if action is not None:
self._drawState()
pygame.time.wait(self.actionDelay)
def reset(self):
GameEnvironment.reset(self)
if hasattr(self, 'screen'):
self.screen.reset()
pygame.display.flip()
