def __init__(self, team_type):
NeroTeam.__init__(self, team_type)
self.pop = OpenNero.Population()
self.rtneat = OpenNero.RTNEAT("data/ai/neat-params.dat", self.pop,
constants.DEFAULT_LIFETIME_MIN,
constants.DEFAULT_EVOLVE_RATE)
self.generation = 1
def __init__(self):
"""
Create the environment
"""
OpenNero.Environment.__init__(self)
self.curr_id = 0
self.max_steps = 20
self.MAX_DIST = math.hypot(constants.XDIM, constants.YDIM)
self.states = {}
self.teams = {}
self.script = 'Hw5/menu.py'
abound = OpenNero.FeatureVectorInfo() # actions
sbound = OpenNero.FeatureVectorInfo() # sensors
rbound = OpenNero.FeatureVectorInfo() # rewards
# actions
abound.add_continuous(
-1, 1
) # forward/backward speed (gets multiplied by constants.MAX_MOVEMENT_SPEED)
abound.add_continuous(
-constants.MAX_TURN_RADIANS,
constants.MAX_TURN_RADIANS) # left/right turn (in radians)
# sensor dimensions
for a in range(constants.N_SENSORS):
sbound.add_continuous(0, 1)
# Rewards
# the enviroment returns the raw multiple dimensions of the fitness as
# they get each step. This then gets combined into, e.g. Z-score, by
# the ScoreHelper in order to calculate the final rtNEAT-fitness
for f in constants.FITNESS_DIMENSIONS:
# we don't care about the bounds of the individual dimensions
rbound.add_continuous(-sys.float_info.max,
sys.float_info.max) # range for reward
# initialize the rtNEAT algorithm parameters
# input layer has enough nodes for all the observations plus a bias
# output layer has enough values for all the actions
# population size matches ours
# 1.0 is the weight initialization noise
rtneat = OpenNero.RTNEAT("data/ai/neat-params.dat",
OpenNero.Population(), constants.pop_size, 1)
key = "rtneat-%s" % constants.OBJECT_TYPE_TEAM_0
OpenNero.set_ai(key, rtneat)
print "get_ai(%s): %s" % (key, OpenNero.get_ai(key))
# set the initial lifetime
lifetime = module.getMod().lt
rtneat.set_lifetime(lifetime)
print 'rtNEAT lifetime:', lifetime
self.agent_info = OpenNero.AgentInitInfo(sbound, abound, rbound)
