def __init__(self):
self.environment = None
self.agent_id = None
self.flag_loc = None
self.flag_id = None
self.set_speedup(constants.DEFAULT_SPEEDUP)
x = constants.XDIM / 2.0
y = constants.YDIM / 3.0
self.spawn_x = {}
self.spawn_y = {}
self.set_spawn(x, y, constants.OBJECT_TYPE_TEAM_0)
self.set_spawn(x, 2 * y, constants.OBJECT_TYPE_TEAM_1)
# Bounds for sensors in neural network and advice language. These bounds are
# used to convert sensor values between network and advice.
self.sbounds_network = OpenNero.FeatureVectorInfo()
self.sbounds_advice = OpenNero.FeatureVectorInfo()
# Networks have better learning bias when cube sensors produce values in the
# range [-1, 1], but the range [0, 1] is more intuitive in the advice
# language. Wall sensors use the same range [0, 1] for both network and advice.
# The sense() method in the ForageEnvironment class use these network bounds
# to scale the sensor values.
for i in range(constants.N_SENSORS):
self.sbounds_network.add_continuous(0, 1)
self.sbounds_advice.add_continuous(0, 1)
# The last sensor is the bias, which always takes the value 1 (upper bound).
self.sbounds_network.add_continuous(0, 1)
self.sbounds_advice.add_continuous(0, 1)
print 'sbounds_network', self.sbounds_network
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)
def agent_info_tuple(self):
abound = OpenNero.FeatureVectorInfo() # actions
sbound = OpenNero.FeatureVectorInfo() # sensors
# actions
abound.add_continuous(-1, 1) # forward/backward speed
abound.add_continuous(-constants.MAX_TURNING_RATE, constants.MAX_TURNING_RATE) # left/right turn (in radians)
abound.add_continuous(0, 1) # fire
abound.add_continuous(0, 1) # omit friend sensors
# sensor dimensions
for a in range(constants.N_SENSORS):
sbound.add_continuous(0, 1)
return sbound, abound, module.rtneat_rewards()
def rtneat_rewards():
"""
Create a reward FeatureVectorInfo to pass to RTNEAT.
"""
reward = OpenNero.FeatureVectorInfo()
for f in constants.FITNESS_DIMENSIONS:
reward.add_continuous(-sys.float_info.max, sys.float_info.max)
return reward
def get_agent_info(self, agent):
"""
return a blueprint for a new agent
"""
for a in constants.WALL_RAY_SENSORS:
agent.add_sensor(OpenNero.RaySensor(
math.cos(math.radians(a)), math.sin(math.radians(a)), 0,
constants.WALL_SENSOR_RADIUS,
constants.OBJECT_TYPE_OBSTACLE,
False))
for a0, a1 in constants.FLAG_RADAR_SENSORS:
agent.add_sensor(OpenNero.RadarSensor(
a0, a1, -90, 90, constants.MAX_VISION_RADIUS,
constants.OBJECT_TYPE_FLAG,
False))
sense = constants.OBJECT_TYPE_TEAM_0
if agent.team_type == sense:
sense = constants.OBJECT_TYPE_TEAM_1
for a0, a1 in constants.ENEMY_RADAR_SENSORS:
agent.add_sensor(OpenNero.RadarSensor(
a0, a1, -90, 90, constants.MAX_VISION_RADIUS,
sense,
False))
for a in constants.TARGETING_SENSORS:
agent.add_sensor(OpenNero.RaySensor(
math.cos(math.radians(a)), math.sin(math.radians(a)), 0,
constants.TARGET_SENSOR_RADIUS,
sense,
False))
abound = OpenNero.FeatureVectorInfo() # actions
sbound = OpenNero.FeatureVectorInfo() # sensors
# actions
abound.add_continuous(-1, 1) # forward/backward speed
abound.add_continuous(-constants.MAX_TURNING_RATE, constants.MAX_TURNING_RATE) # left/right turn (in radians)
abound.add_continuous(0, 1) # fire
abound.add_continuous(0, 1) # omit friend sensors
# sensor dimensions
for a in range(constants.N_SENSORS):
sbound.add_continuous(0, 1)
rbound = OpenNero.FeatureVectorInfo()
rbound.add_continuous(0, 1)
return OpenNero.AgentInitInfo(sbound, abound, rbound)
def start_rtneat(self, pop_size):
" start the rtneat learning demo "
OpenNero.disable_ai()
#self.environment = RoombaEnvironment(constants.XDIM, constants.YDIM, self)
#set_environment(self.environment)
#self.reset_sandbox()
# Create RTNEAT object
rbound = OpenNero.FeatureVectorInfo()
rbound.add_continuous(-sys.float_info.max, sys.float_info.max)
rtneat = OpenNero.RTNEAT("data/ai/neat-params.dat", 2, 1, pop_size, 1.0, rbound, False)
rtneat.set_weight(0,1)
OpenNero.set_ai("rtneat",rtneat)
OpenNero.enable_ai()
self.distribute_bots(pop_size, "data/shapes/roomba/RoombaRTNEAT.xml")
def agent_info_tuple(self):
sbound, abound, _ = NeroAgent.agent_info_tuple(self)
rbound = OpenNero.FeatureVectorInfo() # single-dimension rewards
rbound.add_continuous(-sys.float_info.max, sys.float_info.max)
return sbound, abound, rbound
