def is_episode_over(self, agent):
"""
is the current episode over for the agent?
"""
if agent.group == 'Turret':
return False
team = agent.get_team()
state = self.get_state(agent)
dead = self.hitpoints > 0 and state.total_damage >= self.hitpoints
old = self.lifetime > 0 and agent.step > 0 and 0 == agent.step % self.lifetime
if agent.ai == 'qlearning':
if dead or old:
# simulate a respawn by moving this agent towards the spawn location.
state.total_damage = 0
state.randomize()
agent.state.position = copy.copy(state.initial_position)
agent.state.rotation = copy.copy(state.initial_rotation)
agent.teleport()
return False
rtneat = OpenNero.get_ai("rtneat-%s" % team)
if not rtneat:
rtneat = OpenNero.get_ai("rtneatq-%s" % team)
orphaned = rtneat and not rtneat.has_organism(agent)
return orphaned or dead or old
def spawnAgent(self, team=constants.OBJECT_TYPE_TEAM_0, ai=None):
"""
This is the function ran when an agent already in the field
causes the generation of a new agent.
Returns the id of the spawned agent.
"""
if not self.environment:
return
if ai == 'rtneat' and not OpenNero.get_ai('rtneat-%s' % team):
self.start_rtneat(team)
if ai == 'rtneatq' and not OpenNero.get_ai('rtneatq-%s' % team):
self.start_rtneatq(team)
self.curr_team = team
color = constants.TEAM_LABELS[team]
dx = random.randrange(constants.XDIM / 20) - constants.XDIM / 40
dy = random.randrange(constants.XDIM / 20) - constants.XDIM / 40
return common.addObject(
"data/shapes/character/steve_%s_%s.xml" % (color, ai),
OpenNero.Vector3f(self.spawn_x[team] + dx, self.spawn_y[team] + dy,
2),
type=team)
def ltChange(self, value):
self.environment.lifetime = value
for team in constants.TEAMS:
rtneat = OpenNero.get_ai("rtneat-%s" % team)
if not rtneat:
rtneat = OpenNero.get_ai("rtneatq-%s" % team)
if rtneat:
rtneat.set_lifetime(value)
def ltChange(self, value):
self.environment.lifetime = value
for team in constants.TEAMS:
rtneat = OpenNero.get_ai("rtneat-%s" % team)
if not rtneat:
rtneat = OpenNero.get_ai("rtneatq-%s" % team)
if rtneat:
rtneat.set_lifetime(value)
def set_weight(self, key, value):
self.reward_weights[key] = value
for team in self.teams:
rtneat = OpenNero.get_ai("rtneat-%s" % team)
if not rtneat:
rtneat = OpenNero.get_ai("rtneatq-%s" % team)
if rtneat:
rtneat.set_weight(constants.FITNESS_INDEX[key], value)
def load_team(self, location, team=constants.OBJECT_TYPE_TEAM_0):
NERO.module.NeroModule.load_team(self, location, team)
rtneat = OpenNero.get_ai('rtneat-%s' % team)
if rtneat:
rtneat.set_lifetime(sys.maxint)
rtneat.disable_evolution()
OpenNero.disable_ai() # don't run until button
def load_team(self, location, team=constants.OBJECT_TYPE_TEAM_0):
NERO.module.NeroModule.load_team(self, location, team)
rtneat = OpenNero.get_ai('rtneat-%s' % team)
if rtneat:
rtneat.set_lifetime(sys.maxint)
rtneat.disable_evolution()
OpenNero.disable_ai() # don't run until button
def ltChange(self, value):
self.lt = value
for team in (constants.OBJECT_TYPE_TEAM_0,
constants.OBJECT_TYPE_TEAM_1):
rtneat = OpenNero.get_ai("rtneat-%s" % team)
if rtneat:
rtneat.set_lifetime(value)
def set_weight(self, key, value):
i = constants.FITNESS_INDEX[key]
value = (value - 100) / 100.0 # value in [-1, 1]
for team in (constants.OBJECT_TYPE_TEAM_0, constants.OBJECT_TYPE_TEAM_1):
rtneat = OpenNero.get_ai("rtneat-%s" % team)
if rtneat:
rtneat.set_weight(i, value)
print key, value
def setAdvice(self, advice, team=constants.OBJECT_TYPE_TEAM_0):
""" advice for rtneat agents """
# if there are rtneat agents in the environment, give them some advice
rtneat = OpenNero.get_ai("rtneat-%s" % team)
if not rtneat:
rtneat = OpenNero.get_ai("rtneatq-%s" % team)
if rtneat:
try:
rtneat.advice = OpenNero.Advice(advice, rtneat, constants.N_SENSORS+1, constants.N_ACTIONS, True, self.sbounds_network, self.sbounds_advice)
except RuntimeError as err:
err_msg = \
'<message><content class="edu.utexas.cs.nn.opennero.ErrorMessage"' + \
' name="%s" text="%s" /></message>' % ('Advice Error', err.message)
getServer().write_data(err_msg)
return
for agent in self.environment.teams[team]:
agent.has_new_advice = True
def maybe_spawn(self, agent):
'''Spawn more agents if there are more to spawn.'''
if agent.ai not in ('rtneat', 'rtneatq') or agent.group != 'Agent':
return
team = agent.get_team()
rtneat = OpenNero.get_ai('rtneat-%s' % team)
if not rtneat:
rtneat = OpenNero.get_ai('rtneatq-%s' % team)
if not rtneat or not rtneat.ready():
return
friends, foes = self.getFriendFoe(agent)
if len(friends) >= constants.pop_size:
return
if agent is tuple(f for f in friends if f.ai == agent.ai)[0]:
module.getMod().spawnAgent(team=team, ai=agent.ai)
def set_weight(self, key, value):
i = constants.FITNESS_INDEX[key]
value = (value - 100) / 100.0 # value in [-1, 1]
for team in (constants.OBJECT_TYPE_TEAM_0,
constants.OBJECT_TYPE_TEAM_1):
rtneat = OpenNero.get_ai("rtneat-%s" % team)
if rtneat:
rtneat.set_weight(i, value)
print key, value
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 __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",
constants.N_SENSORS,
constants.N_ACTIONS,
constants.pop_size,
1.0,
rbound, False)
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 save_team(self, location, team=constants.OBJECT_TYPE_TEAM_0):
# if there are rtneat agents in the environment, save them as a group.
rtneat = OpenNero.get_ai("rtneat-%s" % team)
if rtneat:
location = rtneat.save_population(str(location))
# then, check whether there are any qlearning agents, and save them.
with open(location, 'a') as handle:
for agent in self.environment.teams[team]:
if agent.group == 'Agent' and agent.ai == 'qlearning':
handle.write('\n\n%s' % agent.to_string())
if hasattr(agent, 'stats'):
handle.write('\n\n%s' % agent.stats())
def setAdvice(self, advice, team=constants.OBJECT_TYPE_TEAM_0):
""" advice for rtneat agents """
# if there are rtneat agents in the environment, give them some advice
rtneat = OpenNero.get_ai("rtneat-%s" % team)
if not rtneat:
rtneat = OpenNero.get_ai("rtneatq-%s" % team)
if rtneat:
try:
rtneat.advice = OpenNero.Advice(advice, rtneat,
constants.N_SENSORS + 1,
constants.N_ACTIONS, True,
self.sbounds_network,
self.sbounds_advice)
except RuntimeError as err:
err_msg = \
'<message><content class="edu.utexas.cs.nn.opennero.ErrorMessage"' + \
' name="%s" text="%s" /></message>' % ('Advice Error', err.message)
getServer().write_data(err_msg)
return
for agent in self.environment.teams[team]:
agent.has_new_advice = True
def save_team(self, location, team=constants.OBJECT_TYPE_TEAM_0):
# if there are rtneat agents in the environment, save them as a group.
rtneat = OpenNero.get_ai("rtneat-%s" % team)
if rtneat:
location = rtneat.save_population(str(location))
# then, check whether there are any qlearning agents, and save them.
with open(location, 'a') as handle:
for agent in self.environment.teams[team]:
if agent.group == 'Agent' and agent.ai == 'qlearning':
handle.write('\n\n%s' % agent.to_string())
if hasattr(agent, 'stats'):
handle.write('\n\n%s' % agent.stats())
def maybe_spawn(self, agent):
'''Spawn more agents if there are more to spawn.'''
# do not spawn just because a first person agent is on the field
if isinstance(agent, FirstPersonAgent):
return
team = agent.get_team()
friends, foes = self.getFriendFoe(agent)
friends = tuple(friends or [None])
if (agent.group == 'Agent' and agent is friends[0]
and OpenNero.get_ai("rtneat-%s" % team).ready()
and len(friends) < constants.pop_size):
module.getMod().spawnAgent(team)
def maybe_spawn(self, agent):
'''Spawn more agents if there are more to spawn.'''
# do not spawn just because a first person agent is on the field
if isinstance(agent, FirstPersonAgent):
return
team = agent.get_team()
friends, foes = self.getFriendFoe(agent)
friends = tuple(friends or [None])
if (agent.group == 'Agent' and
agent is friends[0] and
OpenNero.get_ai("rtneat-%s" % team).ready() and
len(friends) < constants.pop_size):
module.getMod().spawnAgent(team)
def spawnAgent(self, team=constants.OBJECT_TYPE_TEAM_0, ai=None):
"""
This is the function ran when an agent already in the field
causes the generation of a new agent.
Returns the id of the spawned agent.
"""
if not self.environment:
return
if ai == 'rtneat' and not OpenNero.get_ai('rtneat-%s' % team):
self.start_rtneat(team)
if ai == 'rtneatq' and not OpenNero.get_ai('rtneatq-%s' % team):
self.start_rtneatq(team)
self.curr_team = team
color = constants.TEAM_LABELS[team]
dx = random.randrange(constants.XDIM / 20) - constants.XDIM / 40
dy = random.randrange(constants.XDIM / 20) - constants.XDIM / 40
return common.addObject(
"data/shapes/character/steve_%s_%s.xml" % (color, ai),
OpenNero.Vector3f(self.spawn_x[team] + dx, self.spawn_y[team] + dy, 2),
type=team)
def start_rtneat(self, team=constants.OBJECT_TYPE_TEAM_0):
# 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",
constants.N_SENSORS + 1,
constants.N_ACTIONS, constants.pop_size, 1.0,
rtneat_rewards(), False)
key = "rtneat-%s" % team
OpenNero.set_ai(key, rtneat)
print "get_ai(%s): %s" % (key, OpenNero.get_ai(key))
rtneat.set_lifetime(self.environment.lifetime)
def start_rtneatq(self, team=constants.OBJECT_TYPE_TEAM_0):
# initialize the rtNEAT+Q algorithm parameters
# input layer has enough nodes for all the observations plus a bias
# output layer has enough values for all the wires
# population size matches ours
# 1.0 is the weight initialization noise
rtneatq = OpenNero.RTNEAT("data/ai/neat-params.dat",
constants.N_SENSORS+1,
constants.N_ACTION_CANDIDATES * (constants.N_ACTIONS + 1),
constants.pop_size,
1.0,
rtneat_rewards(),
False)
key = "rtneatq-%s" % team
OpenNero.set_ai(key, rtneatq)
print "get_ai(%s): %s" % (key, OpenNero.get_ai(key))
rtneatq.set_lifetime(self.environment.lifetime)
def is_episode_over(self, agent):
"""
is the current episode over for the agent?
"""
if agent.group == "Turret":
return False
if isinstance(agent, FirstPersonAgent):
return False # first person agents never stop
self.max_steps = module.getMod().lt
if self.max_steps != 0 and agent.step >= self.max_steps:
return True
team = agent.get_team()
if not OpenNero.get_ai("rtneat-%s" % team).has_organism(agent):
return True
state = self.get_state(agent)
if module.getMod().hp != 0 and state.total_damage >= module.getMod().hp:
return True
return False
def save_rtneat(self, location, pop, team=constants.OBJECT_TYPE_TEAM_0):
location = os.path.relpath("/") + location
OpenNero.get_ai("rtneat-%s" % team).save_population(str(location))
def get_org(self):
"""
Returns the rtNEAT object for this agent
"""
return OpenNero.get_ai("rtneat-%s" %
self.get_team()).get_organism(self)
def start_rtneat(self, team=constants.OBJECT_TYPE_TEAM_0):
NERO.module.NeroModule.start_rtneat(self, team)
rtneat = OpenNero.get_ai('rtneat-%s' % team)
if rtneat:
rtneat.set_lifetime(sys.maxint)
rtneat.disable_evolution()
def end(self, time, reward):
"""
end of an episode
"""
OpenNero.get_ai("rtneat-%s" % self.get_team()).release_organism(self)
return True
def start_rtneat(self, team=constants.OBJECT_TYPE_TEAM_0):
NERO.module.NeroModule.start_rtneat(self, team)
rtneat = OpenNero.get_ai('rtneat-%s' % team)
if rtneat:
rtneat.set_lifetime(sys.maxint)
rtneat.disable_evolution()
def get_org(self):
"""
Returns the rtNEAT object for this agent
"""
return OpenNero.get_ai("rtneat-%s" % self.get_team()).get_organism(self)
def end(self, time, reward):
"""
end of an episode
"""
OpenNero.get_ai("rtneat-%s" % self.get_team()).release_organism(self)
return True
def ltChange(self, value):
self.lt = value
for team in (constants.OBJECT_TYPE_TEAM_0, constants.OBJECT_TYPE_TEAM_1):
rtneat = OpenNero.get_ai("rtneat-%s" % team)
if rtneat:
rtneat.set_lifetime(value)
def save_rtneat(self, location, pop, team=constants.OBJECT_TYPE_TEAM_0):
location = os.path.relpath("/") + location
OpenNero.get_ai("rtneat-%s" % team).save_population(str(location))
