def set_display_hint(self):
"""
set the display hint above the agent's head (toggled with F2)
"""
display_hint = constants.getDisplayHint()
if display_hint:
if display_hint == 'fitness':
self.state.label = '%.2f' % self.org.fitness
elif display_hint == 'time alive':
self.state.label = str(self.org.time_alive)
elif display_hint == 'hit points':
self.state.label = ''.join('.' for i in range(int(5*OpenNero.get_environment().get_hitpoints(self))))
elif display_hint == 'id':
self.state.label = str(self.org.genome.id)
elif display_hint == 'champion':
if self.org.champion:
self.state.label = 'champ!'
else:
self.state.label = ''
elif display_hint == 'rank':
self.state.label = str(self.org.rank)
elif display_hint == 'debug':
self.state.label = str(OpenNero.get_environment().get_state(self))
else:
self.state.label = '?'
else:
# the first time we switch away from displaying stuff,
# change the window caption
if self.state.label:
self.state.label = ""
def parseInputCommand(content):
"""
Parse commands from training window
"""
mod = getMod()
command, arg = content.attrib['command'], content.attrib['arg']
# first word is command rest is filename
if command.isupper():
vali = int(arg)
if command == "LT": mod.ltChange(vali)
if command == "EE": mod.eeChange(vali)
if command == "HP": mod.hpChange(vali)
if command == "SP": mod.set_speedup(vali)
if command == "save1": mod.save_team(arg, constants.OBJECT_TYPE_TEAM_0)
if command == "load1": mod.load_team(arg, constants.OBJECT_TYPE_TEAM_0)
if command == "rtneat": mod.deploy('rtneat')
if command == "qlearning": mod.deploy('qlearning')
if command == "pause": OpenNero.disable_ai()
if command == "resume": OpenNero.enable_ai()
if command == "example":
print 'command: example'
if arg == "start":
print 'command: example start'
mod.start_demonstration()
elif arg == "cancel":
print 'command: example cancel'
OpenNero.get_environment().cancel_demonstration()
elif arg == "confirm":
print 'command: example confirm'
OpenNero.get_environment().use_demonstration()
def set_display_hint(self):
"""
set the display hint above the agent's head (toggled with F2)
"""
display_hint = constants.getDisplayHint()
if display_hint:
if display_hint == 'fitness':
self.state.label = '%.2g' % self.fitness[0]
elif display_hint == 'time alive':
self.state.label = str(self.step)
elif display_hint == 'hit points':
self.state.label = ''.join('.' for i in range(
int(5 * OpenNero.get_environment().get_hitpoints(self))))
elif display_hint == 'id':
self.state.label = str(self.state.id)
elif display_hint == 'species id':
self.state.label = 'q'
elif display_hint == 'debug':
self.state.label = str(
OpenNero.get_environment().get_state(self))
else:
self.state.label = '?'
else:
# the first time we switch away from displaying stuff,
# change the window caption
if self.state.label:
self.state.label = ""
def set_display_hint(self):
"""
set the display hint above the agent's head (toggled with F2)
"""
display_hint = constants.getDisplayHint()
if display_hint:
org = self.get_org()
if display_hint == 'fitness':
self.state.label = '%.2f' % org.fitness
elif display_hint == 'time alive':
self.state.label = str(org.time_alive)
elif display_hint == 'hit points':
self.state.label = ''.join('.' for i in range(
int(5 * OpenNero.get_environment().get_hitpoints(self))))
elif display_hint == 'id':
self.state.label = str(org.id)
elif display_hint == 'species id':
self.state.label = str(org.species_id)
elif display_hint == 'champion':
if org.champion:
self.state.label = 'champ!'
else:
self.state.label = ''
elif display_hint == 'rank':
self.state.label = str(org.rank)
elif display_hint == 'debug':
self.state.label = str(
OpenNero.get_environment().get_state(self))
else:
self.state.label = '?'
else:
# the first time we switch away from displaying stuff,
# change the window caption
if self.state.label:
self.state.label = ""
def parseInputCommand(content):
"""
Parse commands from training window
"""
mod = getMod()
command, arg = content.attrib['command'], content.attrib['arg']
# first word is command rest is filename
if command.isupper():
vali = int(arg)
if command == "LT": mod.ltChange(vali)
if command == "EE": mod.eeChange(vali)
if command == "HP": mod.hpChange(vali)
if command == "SP": mod.set_speedup(vali)
if command == "save1": mod.save_team(arg, constants.OBJECT_TYPE_TEAM_0)
if command == "load1": mod.load_team(arg, constants.OBJECT_TYPE_TEAM_0)
if command == "rtneat": mod.deploy('rtneat')
if command == "qlearning": mod.deploy('qlearning')
if command == "pause": OpenNero.disable_ai()
if command == "resume": OpenNero.enable_ai()
if command == "example":
print 'command: example'
if arg == "start":
print 'command: example start'
mod.start_demonstration()
elif arg == "cancel":
print 'command: example cancel'
OpenNero.get_environment().cancel_demonstration()
elif arg == "confirm":
print 'command: example confirm'
OpenNero.get_environment().use_demonstration()
def load_rtneat(self, location , pop, team=constants.OBJECT_TYPE_TEAM_0):
location = os.path.relpath("/") + location
if os.path.exists(location):
OpenNero.set_ai("rtneat-%s" % team, OpenNero.RTNEAT(
str(location), "data/ai/neat-params.dat",
constants.pop_size,
OpenNero.get_environment().agent_info.reward))
def load_rtneat(self, location, pop, team=constants.OBJECT_TYPE_TEAM_0):
location = os.path.relpath("/") + location
if os.path.exists(location):
OpenNero.set_ai(
"rtneat-%s" % team,
OpenNero.RTNEAT(str(location), "data/ai/neat-params.dat",
constants.pop_size,
OpenNero.get_environment().agent_info.reward))
def evaluate_trace(self):
"""
evaluate agent and compute fitness based on trace information
"""
# flush network from previous activations
org = self.get_org()
org.net.flush()
environment = OpenNero.get_environment()
trace = environment.trace
# place the agent at the beginning of the trace
self.state.position = OpenNero.Vector3f(trace.position[0].x,
trace.position[0].y,
trace.position[0].z)
self.state.rotation = OpenNero.Vector3f(trace.rotation[0].x,
trace.rotation[0].y,
trace.rotation[0].z)
current_step = trace.initial_step
j = 0 # trace index at which error is calculated
while j < len(trace.position
) - 1 and current_step < environment.STEPS_PER_EPISODE:
self.state.position = position
self.state.rotation = rotation
sensors = environment.sense(self)
actions = self.network_action(sensors)
# error based on position - find index in trace where error based on
# current position starts to increase, i.e. starting with the current
# trace index, we find position in trace that is closest to the current
# state position.
error1 = trace.position[j].getDistanceFrom(position)
error2 = trace.position[j + 1].getDistanceFrom(position)
while error1 >= error2 and j < len(trace.position) - 2:
j += 1
error1 = error2
error2 = trace.position[j + 1].getDistanceFrom(position)
if error1 > self.ERROR_THRESHOLD:
break
# calculate new position, orientation, and velocity
self.environment.act(self, actions)
current_step += 1
self.passed_steps = j
return float(j) / len(trace.position)
def evaluate_trace(self):
"""
evaluate agent and compute fitness based on trace information
"""
# flush network from previous activations
org = self.get_org()
org.net.flush()
environment = OpenNero.get_environment()
trace = environment.trace
# place the agent at the beginning of the trace
self.state.position = OpenNero.Vector3f(
trace.position[0].x, trace.position[0].y, trace.position[0].z)
self.state.rotation = OpenNero.Vector3f(
trace.rotation[0].x, trace.rotation[0].y, trace.rotation[0].z)
current_step = trace.initial_step
j = 0 # trace index at which error is calculated
while j < len(trace.position)-1 and current_step < environment.STEPS_PER_EPISODE:
self.state.position = position
self.state.rotation = rotation
sensors = environment.sense(self)
actions = self.network_action(sensors)
# error based on position - find index in trace where error based on
# current position starts to increase, i.e. starting with the current
# trace index, we find position in trace that is closest to the current
# state position.
error1 = trace.position[j].getDistanceFrom(position)
error2 = trace.position[j+1].getDistanceFrom(position)
while error1 >= error2 and j < len(trace.position)-2:
j += 1
error1 = error2
error2 = trace.position[j+1].getDistanceFrom(position)
if error1 > self.ERROR_THRESHOLD:
break
# calculate new position, orientation, and velocity
self.environment.act(self, actions)
current_step += 1
self.passed_steps = j
return float(j)/len(trace.position)
def normalize_reward(self, reward):
"""
Combine reward vector into a single value in the range [0,1]
"""
weighted_sum = 0
min_sum = 0
max_sum = 0
environment = OpenNero.get_environment()
for i, f in enumerate(constants.FITNESS_DIMENSIONS):
weight = environment.reward_weights[f]
weighted_sum += weight * reward[i]
min_sum += abs(weight) * -1.0
max_sum += abs(weight)
normalized_reward = weighted_sum
if max_sum > min_sum: #Normalize weighted sum to [0, 1]
d = max_sum - min_sum
normalized_reward = (normalized_reward - min_sum) / d
return normalized_reward
def destroy(self):
env = OpenNero.get_environment()
if env is not None:
env.remove_agent(self)
return True
def destroy(self):
env = OpenNero.get_environment()
if env is not None:
env.remove_agent(self)
return True
