def test_compute_velocity_return_values(swarm, clamp):
"""Test if compute_velocity() gives the expected shape and range"""
topology = Star()
v = topology.compute_velocity(swarm, clamp)
assert v.shape == swarm.position.shape
if clamp is not None:
assert (clamp[0] <= v).all() and (clamp[1] >= v).all()
def evolution_search(f, para_b):
begin_time = datetime.now()
Timestamps_list = []
Target_list = []
Parameters_list = []
keys = list(para_b.keys())
dim = len(keys)
plog = PrintLog(keys)
min = np.ones(dim)
max = np.ones(dim)
value_list = list(parameters.values())
for i_v in range(dim):
min[i_v] = value_list[i_v][0]
max[i_v] = value_list[i_v][1]
bounds = (min, max)
plog.print_header(initialization=True)
my_topology = Star()
my_options ={'c1': 0.6, 'c2': 0.3, 'w': 0.4}
my_swarm = P.create_swarm(n_particles=20, dimensions=dim, options=my_options, bounds=bounds) # The Swarm Class
iterations = 30 # Set 100 iterations
for i in range(iterations):
# Part 1: Update personal best
# for evaluated_result in map(evaluate, my_swarm.position):
# my_swarm.current_cost = np.append(evaluated_result)
# for best_personal_result in map(evaluate, my_swarm.pbest_pos): # Compute personal best pos
# my_swarm.pbest_cost = np.append(my_swarm.pbest_cost, best_personal_result)
my_swarm.current_cost = np.array(list(map(evaluate, my_swarm.position)))
#print(my_swarm.current_cost)
my_swarm.pbest_cost = np.array(list(map(evaluate, my_swarm.pbest_pos)))
my_swarm.pbest_pos, my_swarm.pbest_cost = P.compute_pbest(my_swarm) # Update and store
# Part 2: Update global best
# Note that gbest computation is dependent on your topology
if np.min(my_swarm.pbest_cost) < my_swarm.best_cost:
my_swarm.best_pos, my_swarm.best_cost = my_topology.compute_gbest(my_swarm)
# Let's print our output
#if i % 2 == 0:
# print('Iteration: {} | my_swarm.best_cost: {:.4f}'.format(i + 1, my_swarm.best_cost))
# Part 3: Update position and velocity matrices
# Note that position and velocity updates are dependent on your topology
my_swarm.velocity = my_topology.compute_velocity(my_swarm)
my_swarm.position = my_topology.compute_position(my_swarm)
Parameters_list.append(my_swarm.best_pos.tolist())
Target_list.append(1-my_swarm.best_cost)
elapse_time = (datetime.now() - begin_time).total_seconds()
Timestamps_list.append(elapse_time)
# print("The best candidate: ", my_swarm.best_pos)
# print("The best result: ", res[1])
plog.print_step(my_swarm.best_pos, 1 - my_swarm.best_cost)
if i == 0:
plog.print_header(initialization=False)
return Timestamps_list, Target_list, Parameters_list
def test_compute_position_return_values(swarm, bounds):
"""Test if compute_position() gives the expected shape and range"""
topology = Star()
p = topology.compute_position(swarm, bounds)
assert p.shape == swarm.velocity.shape
if bounds is not None:
assert (bounds[0] <= p).all() and (bounds[1] >= p).all()
def test_compute_gbest_return_values(swarm):
"""Test if compute_gbest() gives the expected return values"""
topology = Star()
expected_cost = 1
expected_pos = np.array([1,2,3])
pos, cost = topology.compute_gbest(swarm)
assert cost == expected_cost
assert (pos == expected_pos).all()
def test_compute_gbest_return_values(swarm):
"""Test if compute_gbest() gives the expected return values"""
topology = Star()
expected_cost = 1.0002528364353296
expected_pos = np.array([9.90438476e-01, 2.50379538e-03, 1.87405987e-05])
pos, cost = topology.compute_gbest(swarm)
assert cost == pytest.approx(expected_cost)
assert (pos == pytest.approx(expected_pos))
def __init__(self):
self.swarm_size = 0
self.my_topology = Star() # connect to n nearest neighbors
self.my_options = {'c1': 4, 'c2': 1, 'w': 0.3}
self.phys_swarm_pos = []
self.logical_swarm = 0 #self.logical_swarm = P.create_swarm(n_particles=0, dimensions=2, options=self.my_options, bounds=([0,0], [self.game_info.map_size[0], self.game_info.map_size[1]]) , init_pos=phys_swarm_pos, clamp=(0,10))
self.iter_of_last_update = 0
self.num_overlords = 0
def __init__(self,
num_params,
c1=0.5 + np.log(2.0),
c2=0.5 + np.log(2.0),
w=0.5 / np.log(2.0),
popsize=256,
sigma_init=0.1,
weight_decay=0.01,
communication_topology='star'):
self.num_params = num_params
self.c1 = c1
self.c2 = c2
self.w = w
self.popsize = popsize
self.sigma_init = sigma_init
self.weight_decay = weight_decay
self.best_param = np.zeros(self.num_params)
self.best_reward = 0
self.pop_params = np.random.randn(self.popsize,
self.num_params) * self.sigma_init
self.pbest_params = self.pop_params
self.pbest_rewards = np.zeros(self.popsize)
self.pop_vel = np.zeros((self.popsize, self.num_params))
self.pop_rewards = np.zeros(self.popsize)
self.gbest_param = self.pop_params[np.argmax(self.pop_rewards)]
self.gbest_reward = np.max(self.pop_rewards)
self.first_iteration = True
# Import backend modules
l_lims = -np.ones(self.num_params)
u_lims = np.ones(self.num_params)
bounds = (l_lims, u_lims)
import pyswarms.backend as P
self.P = P
# Global topology will always be used to compute gbest
from pyswarms.backend.topology import Star
self.global_topology = Star()
self.communication_topology = communication_topology
# Unless specified, use the star topology.
if self.communication_topology == 'random':
from pyswarms.backend.topology import Random
self.topology = Random() # The Topology Class
elif self.communication_topology == 'local':
from pyswarms.backend.topology import Ring
self.topology = Ring() # The Topology Class
else:
from pyswarms.backend.topology import Star
self.topology = Star() # The Topology Class
self.options = {'c1': self.c1, 'c2': self.c2, 'w': self.w}
self.swarm = self.P.create_swarm(n_particles=self.popsize,
dimensions=self.num_params,
options=self.options,
center=self.sigma_init,
bounds=bounds)
def __init__(self):
self.swarm_size = 0
self.my_topology = Star() # connect to n nearest neighbors -> Ring()
#options should be parameterized...
self.my_options = {'c1': 4, 'c2': 1, 'w': 0.3}
# might need dummy values for the following and wait until phys swarm exists to continue...
self.phys_swarm_pos = []
self.log_swarm = \
P.create_swarm(n_particles=0,
dimensions=2,
options=self.my_options,
bounds=([0,0],
[self.game_info.map_size[0], self.game_info.map_size[1]]),
init_pos=phys_swarm_pos, clamp=(0,10))
self.iter_of_last_update = 0
self.num_overlords = 0
#load in cnn model
self.brain = model.load("brain")
def __init__(
self,
n_particles,
dimensions,
options,
bounds=None,
bh_strategy="periodic",
velocity_clamp=None,
vh_strategy="unmodified",
center=1.00,
ftol=-np.inf,
init_pos=None,
):
"""
A custom optimizer modified from pyswarms.single.global_best
https://github.com/ljvmiranda921/pyswarms/blob/master/pyswarms/single/global_best.py
Attributes
----------
n_particles : int
number of particles in the swarm.
dimensions : int
number of dimensions in the space.
options : dict with keys :code:`{'c1', 'c2', 'w'}`
a dictionary containing the parameters for the specific
optimization technique.
* c1 : float
cognitive parameter
* c2 : float
social parameter
* w : float
inertia parameter
bounds : tuple of numpy.ndarray, optional
a tuple of size 2 where the first entry is the minimum bound while
the second entry is the maximum bound. Each array must be of shape
:code:`(dimensions,)`.
bh_strategy : str
a strategy for the handling of out-of-bounds particles.
velocity_clamp : tuple, optional
a tuple of size 2 where the first entry is the minimum velocity and
the second entry is the maximum velocity. It sets the limits for
velocity clamping.
vh_strategy : str
a strategy for the handling of the velocity of out-of-bounds particles.
center : list (default is :code:`None`)
an array of size :code:`dimensions`
ftol : float
relative error in objective_func(best_pos) acceptable for
convergence. Default is :code:`-np.inf`
init_pos : numpy.ndarray, optional
option to explicitly set the particles' initial positions. Set to
:code:`None` if you wish to generate the particles randomly.
"""
super(PSOoptimizer, self).__init__(
n_particles=n_particles,
dimensions=dimensions,
options=options,
bounds=bounds,
velocity_clamp=velocity_clamp,
center=center,
ftol=ftol,
init_pos=init_pos,
)
# Initialize logger
self.rep = Reporter(logger=logging.getLogger(__name__))
# Initialize the resettable attributes
self.reset()
# Initialize the topology
self.top = Star()
self.bh = BoundaryHandler(strategy=bh_strategy)
self.vh = VelocityHandler(strategy=vh_strategy)
self.name = __name__
# Populate memory of the handlers
self.bh.memory = self.swarm.position
self.vh.memory = self.swarm.position
self.swarm.pbest_cost = np.full(self.swarm_size[0], np.inf)
# Set reached requirement
self.reached_requirement = 0
class Legion(sc2.BotAI):
def __init__(self):
self.swarm_size = 0
self.my_topology = Star() # connect to n nearest neighbors
self.my_options = {'c1': 4, 'c2': 1, 'w': 0.3}
self.phys_swarm_pos = []
self.logical_swarm = 0 #self.logical_swarm = P.create_swarm(n_particles=0, dimensions=2, options=self.my_options, bounds=([0,0], [self.game_info.map_size[0], self.game_info.map_size[1]]) , init_pos=phys_swarm_pos, clamp=(0,10))
self.iter_of_last_update = 0
self.num_overlords = 0
# stagger calls per iteration to enhance speed maybe?
async def on_step(self, iteration):
await self.distribute_workers()
await self.build_overlords()
await self.scout()
await self.build_workers()
await self.build_queens()
await self.inject_larva()
await self.build_extractor()
await self.build_offensive_buildings()
await self.expand()
await self.upgrade()
await self.build_swarm()
if self.units(MUTALISK).exists and iteration % 5 == 0:
await self.attack(self.units(MUTALISK), iteration)
# if self.units(ZERGLING).amount > 35 and iteration % 10 == 0:
# await self.attack(self.units(ZERGLING))
if self.units(ROACH).amount > 5 and iteration % 4 == 0:
await self.attack(self.units(ROACH), iteration)
# figure out a way for the overlords to not send themselves to their death...
async def scout(self):
if self.num_overlords != self.units(OVERLORD).amount:
self.num_overlords = self.units(OVERLORD).amount
orders = []
for unit, pos in zip(self.units(OVERLORD),
self.expansion_locations):
orders.append(unit.move(pos))
await self.do_actions(orders)
# reverse these if statements to check existance before cost...
async def build_offensive_buildings(self):
hatcheries = self.townhalls.ready
if self.can_afford(SPAWNINGPOOL) and not self.units(
SPAWNINGPOOL).exists and not self.already_pending(
SPAWNINGPOOL):
await self.build(SPAWNINGPOOL, near=hatcheries.first)
return
# I want to build this near the main ramp...
# if self.can_afford(SPINECRAWLER) and self.units(SPINECRAWLER).amount < 4:
# await self.build(SPINECRAWLER, near=hatcheries.first, max_distance=8)
# return
if self.can_afford(ROACHWARREN) and self.units(
SPAWNINGPOOL
).ready and not self.units(
ROACHWARREN).exists and not self.already_pending(ROACHWARREN):
await self.build(ROACHWARREN, near=hatcheries.first)
return
if self.can_afford(EVOLUTIONCHAMBER) and len(
self.units(EVOLUTIONCHAMBER)) < 1:
await self.build(EVOLUTIONCHAMBER, near=hatcheries.first)
if self.can_afford(LAIR) and not self.units(
LAIR).exists and not self.already_pending(LAIR):
await self.do(self.townhalls.first.build(LAIR))
return
if self.can_afford(SPIRE) and self.units(
SPIRE).amount < 2 and not self.already_pending(SPIRE):
await self.build(SPIRE, near=hatcheries.first)
return
if self.can_afford(INFESTATIONPIT) and self.units(
LAIR).ready and not self.units(
INFESTATIONPIT).exists and not self.already_pending(
INFESTATIONPIT):
await self.build(INFESTATIONPIT, near=hatcheries.first)
return
if self.can_afford(HIVE) and not self.units(
HIVE).exists and not self.already_pending(HIVE):
await self.do(self.townhalls.first.build(HIVE))
return
if self.can_afford(GREATERSPIRE) and self.units(HIVE).ready \
and self.units(SPIRE).ready and not self.already_pending(GREATERSPIRE) and not self.units(GREATERSPIRE).exists:
await self.do(self.units(SPIRE).first.build(GREATERSPIRE))
# more succint way of writing this... refactor this!!!
# check timing to ensure spire upgrades dont retard others...
# incorporate the army specific upgrades for roaches and hydras
async def upgrade(self):
if self.units(EVOLUTIONCHAMBER).ready.idle.exists:
for evo in self.units(EVOLUTIONCHAMBER).ready.idle:
abilities = await self.get_available_abilities(evo)
targetAbilities = [
AbilityId.RESEARCH_ZERGMISSILEWEAPONSLEVEL1,
AbilityId.RESEARCH_ZERGMISSILEWEAPONSLEVEL2,
AbilityId.RESEARCH_ZERGMISSILEWEAPONSLEVEL3,
AbilityId.RESEARCH_ZERGGROUNDARMORLEVEL1,
AbilityId.RESEARCH_ZERGGROUNDARMORLEVEL2,
AbilityId.RESEARCH_ZERGGROUNDARMORLEVEL3
]
if self.units(GREATERSPIRE).exists:
targetAbilities.extend([
AbilityId.RESEARCH_ZERGMELEEWEAPONSLEVEL1,
AbilityId.RESEARCH_ZERGMELEEWEAPONSLEVEL2,
AbilityId.RESEARCH_ZERGMELEEWEAPONSLEVEL3
])
for ability in targetAbilities:
if ability in abilities:
if self.can_afford(ability):
err = await self.do(evo(ability))
if not err:
break
if self.units(GREATERSPIRE).ready.idle.exists:
gs = self.units(GREATERSPIRE).ready.idle.random
abilities = await self.get_available_abilities(gs)
targetAbilities = [
AbilityId.RESEARCH_ZERGFLYERATTACKLEVEL1,
AbilityId.RESEARCH_ZERGFLYERATTACKLEVEL2,
AbilityId.RESEARCH_ZERGFLYERATTACKLEVEL3,
AbilityId.RESEARCH_ZERGFLYERARMORLEVEL1,
AbilityId.RESEARCH_ZERGFLYERARMORLEVEL2,
AbilityId.RESEARCH_ZERGFLYERARMORLEVEL3
]
for ability in targetAbilities:
if self.can_afford(ability) and ability in abilities:
err = await self.do(gs(ability))
if not err:
break
# FUTURE: incorporate dynamic composition by either tech level or AI/ML
async def build_swarm(self):
if self.units(SPAWNINGPOOL).ready:
larvae = self.units(LARVA).ready.noqueue
for larva in larvae:
if self.supply_left > 0:
if self.units(SPIRE).ready:
# if self.units(CORRUPTOR).amount < 5 and self.can_afford(CORRUPTOR):
# await self.do(larva.train(CORRUPTOR))
# continue
# if self.units(HIVE).ready and self.units(BROODLORD).amount < 5 and self.can_afford(BROODLORD):
# await self.do(self.units(CORRUPTOR).random.train(BROODLORD))
if self.can_afford(
MUTALISK
) and self.supply_left > 2: # 30 muta = 60 c, possibly set to max cap...
await self.do(larva.train(MUTALISK))
continue
if self.units(
ROACHWARREN
).ready and self.supply_left > 2 and self.units(
ROACH).amount < 30 and self.can_afford(
ROACH): # 30 roach = 60 c
await self.do(larva.train(ROACH))
continue
# if self.units(ZERGLING).amount < 60 and self.can_afford(ZERGLING): # 60 lings = 30 c
# await self.do(larva.train(ZERGLING))
# continue
# This could be more succinct...
#need to consider a separate swarm list in order to allow damaged units to RTB
# investigate multithreading and running each unit in own thread to avoid a double for loop...
# might need to pursue subswarms or multiple swarms to avoid reinitializing the swarm so often
# there is a parameter for multiprocessing...
# This algo might only work for flying units.
#might have to build a multiswarm architecture for overlords to pass data to mutalisks...
# investigate periodic reinitialization to break convergance after defeating enemy army...
async def attack(self, phys_swarm, iteration):
if phys_swarm.amount > 3:
orders = []
# reinitialize swarm if needed
if phys_swarm.amount > self.swarm_size + 3 or iteration > self.iter_of_last_update + 75:
self.swarm_size = phys_swarm.amount
self.phys_swarm_pos = []
for unit in phys_swarm:
self.phys_swarm_pos.append(
[unit.position.x, unit.position.y])
self.phys_swarm_pos = np.array(self.phys_swarm_pos)
self.logical_swarm = P.create_swarm(
n_particles=phys_swarm.amount,
dimensions=2,
options=self.my_options,
bounds=([0, 0], [
self.game_info.map_size[0], self.game_info.map_size[1]
]),
init_pos=self.phys_swarm_pos,
clamp=(0, 4.0))
self.iter_of_last_update = iteration
self.logical_swarm.current_cost = self.fitness(
self.logical_swarm.position, phys_swarm)
self.logical_swarm.pbest_cost = self.fitness(
self.logical_swarm.pbest_pos, phys_swarm)
self.logical_swarm.pbest_pos, self.logical_swarm.pbest_cost = P.compute_pbest(
self.logical_swarm)
if np.min(self.logical_swarm.pbest_cost
) < self.logical_swarm.best_cost:
self.logical_swarm.best_pos, self.logical_swarm.best_cost = self.my_topology.compute_gbest(
self.logical_swarm)
self.logical_swarm.velocity = self.my_topology.compute_velocity(
self.logical_swarm)
self.logical_swarm.position = self.my_topology.compute_position(
self.logical_swarm)
# Extract positions from above and issue movement/attack orders
# loop through np array compiling positions and appending them to orders list.
# I havent seen this behavior....
# wounded_units = phys_swarm.filter(lambda u: u.health_percentage <= .7)
# phys_swarm = phys_swarm.filter(lambda u: u.health_percentage > .7)
# for unit in wounded_units:
# unit.move(self.townhalls.first.position)
# print("Retreating wounded unit")
# The mutas are still ignoring nearby enemies.
for row, unit in zip(self.logical_swarm.position, phys_swarm):
if self.known_enemy_units.closer_than(unit.radar_range,
unit.position).exists:
orders.append(unit.stop())
orders.append(
unit.attack(
self.known_enemy_units.closest_to(
unit.position).position))
elif self.known_enemy_units.closer_than(
20, Point2(Pointlike((row[0], row[1])))).exists:
orders.append(
unit.attack(
self.known_enemy_units.closest_to(
Point2(Pointlike((row[0], row[1]))))))
else:
orders.append(
unit.move(Point2(Pointlike((row[0], row[1])))))
await self.do_actions(orders)
def fitness(self, logical_swarm_pos, phys_swarm):
#account for own health, enemy health/splash&anti air damage/
# upgrade this function...
# revert this back to a regular one unit function and apply the pyswarm evaluate method
cost = []
# the coefficients within can be optimized...
if self.known_enemy_units.exists:
for logical_pos in logical_swarm_pos:
target_point = Point2(
Pointlike((logical_pos[0], logical_pos[1])))
cost.append( \
(1-(10/phys_swarm.amount))*self.known_enemy_units.closest_distance_to(target_point) + \
(10/phys_swarm.amount)*self.townhalls.first.distance_to(target_point) + \
(2 * self.known_enemy_units.closer_than(10, target_point).amount - phys_swarm.closer_than(10, target_point).amount) \
)
else:
for logical_pos in logical_swarm_pos:
cost.append(40 / phys_swarm.center.distance2_to(
Point2(Pointlike((logical_pos[0], logical_pos[1])))))
return np.array(cost)
# Economic Functions
async def build_workers(self):
for larva in self.units(LARVA).ready.noqueue:
if self.can_afford(
DRONE
) and self.workers.amount < self.townhalls.amount * 16 and self.workers.amount < 80:
await self.do(larva.train(DRONE))
async def build_queens(self):
if self.units(SPAWNINGPOOL).ready.exists:
for hatchery in self.units(HATCHERY).ready.noqueue:
if self.can_afford(QUEEN) and self.units(
QUEEN).amount < self.townhalls.amount:
await self.do(hatchery.train(QUEEN))
async def inject_larva(self):
for queen, hatchery in zip(
self.units(QUEEN).idle, self.townhalls.ready):
abilities = await self.get_available_abilities(queen)
if AbilityId.EFFECT_INJECTLARVA in abilities:
await self.do(queen(EFFECT_INJECTLARVA, hatchery))
async def build_overlords(self):
if self.supply_left < 9 and self.units(OVERLORD).amount < 26:
larvae = self.units(LARVA).ready.noqueue
for larva in larvae:
if self.can_afford(
OVERLORD) and not self.already_pending(OVERLORD):
await self.do(larva.train(OVERLORD))
async def build_extractor(self):
for hatchery in self.units(HATCHERY).ready:
vespene = self.state.vespene_geyser.closer_than(15.0, hatchery)
for v in vespene:
if not self.can_afford(EXTRACTOR):
break
worker = self.select_build_worker(v.position)
if worker is None:
break
if not self.units(EXTRACTOR).closer_than(1.0, v).exists:
await self.do(worker.build(EXTRACTOR, v))
# upgrade the logic of this to expand when a base becomes exhausted...
async def expand(self):
if self.townhalls.amount < 3 and self.can_afford(HATCHERY):
await self.expand_now()
return fAcc_test
#------------------------------------------------------------------------------
############################# PSO Algorithm ##################################
#------------------------------------------------------------------------------
# ---------------------- Rjb
# Parameter setting
PSO_hyper = {
'Max_iter' : 50, # number of neurons in a layer
'Pop_size' : 30, # activation function
'dimensions' : 2, # number of neurons in a layer
'c1' : 0.5, # activation function
'c2' : 0.3, # number of neurons in a layer
'w' : 0.9, # Mean squared error
}
# Optimization of FFNN1 --------------------------------> best pos = [498.98, 519.548]
my_topology = Star() # The Topology Class
options = {'c1': PSO_hyper['c1'], 'c2': PSO_hyper['c2'], 'w':PSO_hyper['w']} # Set-up hyperparameters
bounds=[(X_train1.shape[0],X_train1.shape[0]), (2*X_train1.shape[0]+1,2*X_train1.shape[0]+1)] # input bounds [(x1_min,x1_max),(x2_min,x2_max)...]
optimizer = ps.single.GlobalBestPSO(n_particles=PSO_hyper['Pop_size'], dimensions=PSO_hyper['dimensions'],
options=options, bounds=bounds)
cost1, pos1 = optimizer.optimize(F1_1, iters=PSO_hyper['Max_iter'])
# Parameter setting
PSO_hyper = {
'Max_iter' : 50, # number of neurons in a layer
'Pop_size' : 30, # activation function
'dimensions' : 2, # number of neurons in a layer
'c1' : 0.5, # activation function
'c2' : 0.3, # number of neurons in a layer
'w' : 0.9, # Mean squared error
}
# Optimization of FFNN2 --------------------------------> best pos = [9.229, 11.102]
@pytest.fixture(scope="module")
def binary_reset():
"""Returns a BinaryPSO instance that has been run and reset to check
default value"""
pso = BinaryPSO(10, 2, {"c1": 0.5, "c2": 0.7, "w": 0.5, "k": 2, "p": 2})
pso.optimize(sphere_func, 10, verbose=0)
pso.reset()
return pso
@pytest.fixture
def options():
"""Default options dictionary for most PSO use-cases"""
options_ = {"c1": 0.5, "c2": 0.7, "w": 0.5, "k": 2, "p": 2, "r": 1}
return options_
@pytest.fixture(params=[
Star(),
Ring(static=False), Ring(static=True),
Pyramid(static=False), Pyramid(static=True),
Random(static=False), Random(static=True),
VonNeumann()
])
def topology(request):
"""Parametrized topology parameter"""
topology_ = request.param
return topology_
def PSO_grad_optimize(n_particles, dimensions, bounds, init_pose, select_num,
iteration, lr, init_state, init_act):
#Input:
# n_particles:num of particles
#dimension: dim of varibales
#bounds: boundary of actions
#init_pose: initial pose
#select_num: num of particle kept
#iteration: number of iterations
#lr: learning rate of apply gradient
#init_state: initial state input for gradient compute
#init_act: initial action input for gradient compute
#output:
#the best pose
#a list of selected pos
#define partcicle
my_topology = Star() # The Topology Class
my_options = {
'c1': 0.1,
'c2': 0.000,
'w': 0.000
} # arbitrarily set #0.01,0.01
my_swarm = P.create_swarm(n_particles=n_particles,
dimensions=dimensions,
options=my_options,
bounds=bounds,
init_pos=init_pose) # The Swarm Class
for i in range(iteration):
# Part 1: Update personal best
step_num = int(dimensions / actor_critic.act_dim)
my_swarm.current_cost = f(my_swarm.position)
cur_action = my_swarm.position.reshape(
(n_particles, step_num, actor_critic.act_dim))
gradients = actor_critic.compute_gradient(step_num, init_act,
init_state, cur_action)
my_swarm.pbest_pos = cur_action.reshape(
(-1, dimensions)) + gradients[0].reshape((-1, dimensions)) * lr
my_swarm.pbest_pos = np.clip(my_swarm.pbest_pos, -0.015, 0.015)
my_swarm.pbest_cost = f(my_swarm.pbest_pos)
print(my_swarm.pbest_cost)
# Part 2: Update global best
# Note that gbest computation is dependent on your topology
if np.min(my_swarm.pbest_cost) < my_swarm.best_cost:
my_swarm.best_pos, my_swarm.best_cost = my_topology.compute_gbest(
my_swarm)
# Let's print our output
print('Iteration: {} | my_swarm.best_cost: {:.4f}'.format(
i + 1, my_swarm.best_cost))
# Part 3: Update position and velocity matrices
# Note that position and velocity updates are dependent on your topology
my_swarm.velocity = my_topology.compute_velocity(my_swarm)
my_swarm.position = my_topology.compute_position(my_swarm)
my_swarm.position = np.clip(my_swarm.position, -0.015, 0.015)
best_index = my_swarm.pbest_cost.argsort()[:select_num]
print(best_index)
good_actions = my_swarm.pbest_pos[best_index].tolist()
good_actions.append(my_swarm.best_pos)
return np.clip(my_swarm.best_pos, -0.015, 0.015), np.asarray(good_actions)
def test_neighbor_idx(swarm):
"""Test if the neighbor_idx attribute is assigned"""
topology = Star()
topology.compute_gbest(swarm)
assert topology.neighbor_idx is not None
def optimizer(self, request):
global parameters
if request.param.__name__ == "GeneralOptimizerPSO":
return request.param, {**parameters, **{"topology": Star()}}
return request.param, parameters
class PSOoptimizer(SwarmOptimizer):
def __init__(
self,
n_particles,
dimensions,
options,
bounds=None,
bh_strategy="periodic",
velocity_clamp=None,
vh_strategy="unmodified",
center=1.00,
ftol=-np.inf,
init_pos=None,
):
"""
A custom optimizer modified from pyswarms.single.global_best
https://github.com/ljvmiranda921/pyswarms/blob/master/pyswarms/single/global_best.py
Attributes
----------
n_particles : int
number of particles in the swarm.
dimensions : int
number of dimensions in the space.
options : dict with keys :code:`{'c1', 'c2', 'w'}`
a dictionary containing the parameters for the specific
optimization technique.
* c1 : float
cognitive parameter
* c2 : float
social parameter
* w : float
inertia parameter
bounds : tuple of numpy.ndarray, optional
a tuple of size 2 where the first entry is the minimum bound while
the second entry is the maximum bound. Each array must be of shape
:code:`(dimensions,)`.
bh_strategy : str
a strategy for the handling of out-of-bounds particles.
velocity_clamp : tuple, optional
a tuple of size 2 where the first entry is the minimum velocity and
the second entry is the maximum velocity. It sets the limits for
velocity clamping.
vh_strategy : str
a strategy for the handling of the velocity of out-of-bounds particles.
center : list (default is :code:`None`)
an array of size :code:`dimensions`
ftol : float
relative error in objective_func(best_pos) acceptable for
convergence. Default is :code:`-np.inf`
init_pos : numpy.ndarray, optional
option to explicitly set the particles' initial positions. Set to
:code:`None` if you wish to generate the particles randomly.
"""
super(PSOoptimizer, self).__init__(
n_particles=n_particles,
dimensions=dimensions,
options=options,
bounds=bounds,
velocity_clamp=velocity_clamp,
center=center,
ftol=ftol,
init_pos=init_pos,
)
# Initialize logger
self.rep = Reporter(logger=logging.getLogger(__name__))
# Initialize the resettable attributes
self.reset()
# Initialize the topology
self.top = Star()
self.bh = BoundaryHandler(strategy=bh_strategy)
self.vh = VelocityHandler(strategy=vh_strategy)
self.name = __name__
# Populate memory of the handlers
self.bh.memory = self.swarm.position
self.vh.memory = self.swarm.position
self.swarm.pbest_cost = np.full(self.swarm_size[0], np.inf)
# Set reached requirement
self.reached_requirement = 0
def get_current_pos(self):
return self.swarm.position
def update(self, iters, current_cost, **kwargs):
"""
Optimize the swarm for one iteration by providing its cost
manually.
Parameters
----------
iters : int
the current iterations
current_cost : ndarray
the current cost which should be provided
"""
self.rep.log("Obj. func. args: {}".format(kwargs), lvl=logging.DEBUG)
self.rep.log(
"Optimize for {} iters with {}".format(iters, self.options),
lvl=logging.DEBUG,
)
self.swarm.current_cost = current_cost
self.swarm.pbest_pos, self.swarm.pbest_cost = compute_pbest(self.swarm)
# Set best_cost_yet_found for ftol
best_cost_yet_found = self.swarm.best_cost
self.swarm.best_pos, self.swarm.best_cost = self.top.compute_gbest(
self.swarm)
# fmt: on
# self.rep.hook(best_cost=self.swarm.best_cost)
# Save to history
hist = self.ToHistory(
best_cost=self.swarm.best_cost,
mean_pbest_cost=np.mean(self.swarm.pbest_cost),
mean_neighbor_cost=self.swarm.best_cost,
position=self.swarm.position,
velocity=self.swarm.velocity,
)
self._populate_history(hist)
# Verify stop criteria based on the relative acceptable cost ftol
relative_measure = self.ftol * (1 + np.abs(best_cost_yet_found))
if (np.abs(self.swarm.best_cost - best_cost_yet_found) <
relative_measure):
self.reached_requirement = 1
# Perform velocity and position updates
self.swarm.velocity = self.top.compute_velocity(
self.swarm, self.velocity_clamp, self.vh, self.bounds)
self.swarm.position = self.top.compute_position(
self.swarm, self.bounds, self.bh)
def finalize(self):
"""
Obtain the final best_cost and the final best_position
"""
final_best_cost = self.swarm.best_cost.copy()
final_best_pos = self.swarm.pbest_pos[
self.swarm.pbest_cost.argmin()].copy()
# Write report in log and return final cost and position
self.rep.log(
"Optimization finished | best cost: {}, best pos: {}".format(
final_best_cost, final_best_pos),
lvl=logging.INFO,
)
return (final_best_cost, final_best_pos)
def optimize(self, objective_func, iters, **kwargs):
#
for _iter in range(iters):
swarm_pos = self.get_current_pos()
current_cost = objective_func(swarm_pos, **kwargs)
self.update(_iter, current_cost)
return self.finalize()
class Legion(sc2.BotAI):
def __init__(self):
self.swarm_size = 0
self.my_topology = Star() # connect to n nearest neighbors
self.my_options = {'c1': 4, 'c2': 1, 'w': 0.3}
self.phys_swarm_pos = []
self.logical_swarm = 0 #self.logical_swarm = P.create_swarm(n_particles=0, dimensions=2, options=self.my_options, bounds=([0,0], [self.game_info.map_size[0], self.game_info.map_size[1]]) , init_pos=phys_swarm_pos, clamp=(0,10))
# stagger calls per iteration to enhance speed maybe?
async def on_step(self, iteration):
await self.distribute_workers()
await self.build_overlords()
await self.build_workers()
await self.build_queens()
await self.inject_larva()
await self.build_extractor()
await self.build_offensive_buildings()
await self.expand()
await self.build_swarm()
if self.units(MUTALISK).exists and iteration % 5 == 0:
await self.attack(self.units(MUTALISK))
# reverse these if statements to check existance before cost...
async def build_offensive_buildings(self):
hatcheries = self.townhalls.ready
if self.can_afford(SPAWNINGPOOL) and not self.units(SPAWNINGPOOL).exists:
await self.build(SPAWNINGPOOL, near=hatcheries.first)
return
# this needs adjusted to check for spawning pool...
if self.can_afford(ROACHWARREN) and self.units(SPAWNINGPOOL).ready and not self.units(ROACHWARREN).exists:
await self.build(ROACHWARREN, near=hatcheries.first)
return
if self.can_afford(LAIR) and not self.units(LAIR).exists:
await self.do(self.townhalls.first.build(LAIR))
return
if self.can_afford(SPIRE) and not self.units(SPIRE).exists:
await self.build(SPIRE, near=hatcheries.first)
return
if self.can_afford(INFESTATIONPIT) and self.units(LAIR).ready and not self.units(INFESTATIONPIT).exists:
await self.build(INFESTATIONPIT, near=hatcheries.first)
return
if self.can_afford(HIVE) and not self.units(HIVE).exists:
await self.do(self.townhalls.first.build(HIVE))
return
# This probably can be reduced to a 2 argument statement... VVVV
if self.can_afford(GREATERSPIRE) and self.units(HIVE).ready \
and self.units(SPIRE).ready and not self.units(GREATERSPIRE).exists:
await self.do(self.units(SPIRE).first.build(GREATERSPIRE))
# FUTURE: incorporate dynamic composition by either tech level or AI/ML
async def build_swarm(self):
if self.units(SPAWNINGPOOL).ready:
larvae = self.units(LARVA).ready.noqueue
for larva in larvae:
if self.supply_left > 0:
if self.units(SPIRE).ready:
# if self.units(CORRUPTOR).amount < 5 and self.can_afford(CORRUPTOR):
# await self.do(larva.train(CORRUPTOR))
# continue
# elif self.units(HIVE).ready and self.units(BROODLORD).amount < 5 and self.can_afford(BROODLORD):
# await self.do(self.units(CORRUPTOR).random.train(BROODLORD))
if self.can_afford(MUTALISK): # 30 muta = 60 c, possibly set to max cap...
await self.do(larva.train(MUTALISK))
continue
# if self.units(ROACHWARREN).ready and self.units(ROACH).amount < 30 and self.can_afford(ROACH): # 30 roach = 60 c
# await self.do(larva.train(ROACH))
# continue
# if self.units(ZERGLING).amount < 60 and self.can_afford(ZERGLING): # 60 lings = 30 c
# await self.do(larva.train(ZERGLING))
# continue
#need to consider a separate swarm list in order to allow damaged units to RTB
# investigate multithreading and running each unit in own thread to avoid a double for loop...
# might need to pursue subswarms or multiple swarms to avoid reinitializing the swarm so often
# there is a parameter for multiprocessing...
# This algo might only work for flying units.
#might have to build a multiswarm architecture for overlords to pass data to mutalisks...
async def attack(self, phys_swarm):
if phys_swarm.amount > 10:
orders = []
# reinitialize swarm if needed
if phys_swarm.amount > self.swarm_size + 3:
self.swarm_size = phys_swarm.amount
self.phys_swarm_pos = []
for unit in phys_swarm:
self.phys_swarm_pos.append([unit.position.x, unit.position.y])
self.phys_swarm_pos = np.array(self.phys_swarm_pos)
self.logical_swarm = P.create_swarm(n_particles=phys_swarm.amount, dimensions=2, options=self.my_options, bounds=([0,0], [self.game_info.map_size[0], self.game_info.map_size[1]]) , init_pos=self.phys_swarm_pos, clamp=(0,4.0))
self.logical_swarm.current_cost = self.fitness(self.logical_swarm.position, phys_swarm)
self.logical_swarm.pbest_cost = self.fitness(self.logical_swarm.pbest_pos, phys_swarm)
self.logical_swarm.pbest_pos, self.logical_swarm.pbest_cost = P.compute_pbest(self.logical_swarm)
if np.min(self.logical_swarm.pbest_cost) < self.logical_swarm.best_cost:
self.logical_swarm.best_pos, self.logical_swarm.best_cost = self.my_topology.compute_gbest(self.logical_swarm)
self.logical_swarm.velocity = self.my_topology.compute_velocity(self.logical_swarm)
self.logical_swarm.position = self.my_topology.compute_position(self.logical_swarm)
# Extract positions from above and issue movement/attack orders
# loop through np array compiling positions and appending them to orders list.
# The mutas are still ignoring nearby enemies.
for row, unit in zip(self.logical_swarm.position, phys_swarm):
if self.known_enemy_units.closer_than(unit.radar_range, unit.position).exists:
orders.append(unit.stop())
orders.append(unit.attack(self.known_enemy_units.closest_to(unit.position).position))
elif self.known_enemy_units.exists:
orders.append(unit.attack(self.known_enemy_units.closest_to(Point2(Pointlike((row[0], row[1]))))))
else:
orders.append(unit.move(Point2(Pointlike((row[0], row[1])))))
# if self.known_enemy_units.in_attack_range_of(unit).exists:
# orders.append(unit.attack(self.known_enemy_units.closest_to(unit.position).position))
await self.do_actions(orders)
def fitness(self, logical_swarm_pos, phys_swarm):
#account for own health, enemy health/splash&anti air damage/
# upgrade this function...
# revert this back to a regular one unit function and apply the pyswarm evaluate method
cost = []
if self.known_enemy_units.exists:
for logical_pos in logical_swarm_pos:
cost.append(self.known_enemy_units.closest_distance_to(Point2(Pointlike((logical_pos[0], logical_pos[1])))))
else:
for logical_pos in logical_swarm_pos:
cost.append(1 / phys_swarm.center.distance2_to(Point2(Pointlike((logical_pos[0], logical_pos[1])))))
return np.array(cost)
#scouting... else:
# for logical_pos in logical_swarm_pos:
# cost.append(self.known_enemy_units.closest_distance_to(Point2(Pointlike(logical_pos[0], logical_pos[1]))))
# Economic Functions
# Theres a bug resulting in overproduction of overlords and workers as well as 2 extra expansions....
async def build_workers(self):
for larva in self.units(LARVA).ready.noqueue:
if self.can_afford(DRONE) and self.workers.amount < self.townhalls.amount * 12:
await self.do(larva.train(DRONE))
async def build_queens(self):
if self.units(SPAWNINGPOOL).ready.exists:
for hatchery in self.units(HATCHERY).ready.noqueue:
if self.can_afford(QUEEN) and self.units(QUEEN).amount < self.townhalls.amount:
await self.do(hatchery.train(QUEEN))
async def inject_larva(self):
for queen, hatchery in zip(self.units(QUEEN).idle, self.townhalls.ready):
abilities = await self.get_available_abilities(queen)
if AbilityId.EFFECT_INJECTLARVA in abilities:
await self.do(queen(EFFECT_INJECTLARVA, hatchery))
async def build_overlords(self):
if self.supply_left < 9 and self.units(OVERLORD).amount < 26:
larvae = self.units(LARVA).ready.noqueue
for larva in larvae:
if self.can_afford(OVERLORD) and not self.already_pending(OVERLORD):
await self.do(larva.train(OVERLORD))
async def build_extractor(self):
for hatchery in self.units(HATCHERY).ready:
vespene = self.state.vespene_geyser.closer_than(15.0, hatchery)
for v in vespene:
if not self.can_afford(EXTRACTOR):
break
worker = self.select_build_worker(v.position)
if worker is None:
break
if not self.units(EXTRACTOR).closer_than(1.0, v).exists:
await self.do(worker.build(EXTRACTOR, v))
# upgrade the logic of this to expand when a base becomes exhausted...
async def expand(self):
if self.townhalls.amount < 5 and self.can_afford(HATCHERY):
await self.expand_now()
dimensions=dim,
options=options,
velocity_clamp=clamp,
topology=my_topology)
elif (topoModel == "random"):
from pyswarms.backend.topology import Random as PSOTopoRandom
my_topology = PSOTopoRandom()
options = {'c1': c1, 'c2': c2, 'w': w, 'k': k, 'p': p}
optimizer = ps.single.GeneralOptimizerPSO(n_particles=swarm_size,
dimensions=dim,
options=options,
velocity_clamp=clamp,
topology=my_topology)
elif (topoModel == "star"):
from pyswarms.backend.topology import Star
my_topology = Star(static=False)
optimizer = ps.single.GeneralOptimizerPSO(n_particles=swarm_size,
dimensions=dim,
options=options,
velocity_clamp=clamp,
topology=my_topology)
elif (topoModel == "local"):
options = {'c1': c1, 'c2': c2, 'w': w, 'k': k, 'p': p}
optimizer = ps.single.LocalBestPSO(n_particles=swarm_size,
dimensions=dim,
options=options,
velocity_clamp=clamp)
else:
print("Chose global best with ", swarm_size, " dims : ", dim,
"options : ", options)
print()
class Pyswarms:
''' Pyswarms Wrapper '''
def __init__(self,
num_params,
c1=0.5 + np.log(2.0),
c2=0.5 + np.log(2.0),
w=0.5 / np.log(2.0),
popsize=256,
sigma_init=0.1,
weight_decay=0.01,
communication_topology='star'):
self.num_params = num_params
self.c1 = c1
self.c2 = c2
self.w = w
self.popsize = popsize
self.sigma_init = sigma_init
self.weight_decay = weight_decay
self.best_param = np.zeros(self.num_params)
self.best_reward = 0
self.pop_params = np.random.randn(self.popsize,
self.num_params) * self.sigma_init
self.pbest_params = self.pop_params
self.pbest_rewards = np.zeros(self.popsize)
self.pop_vel = np.zeros((self.popsize, self.num_params))
self.pop_rewards = np.zeros(self.popsize)
self.gbest_param = self.pop_params[np.argmax(self.pop_rewards)]
self.gbest_reward = np.max(self.pop_rewards)
self.first_iteration = True
# Import backend modules
l_lims = -np.ones(self.num_params)
u_lims = np.ones(self.num_params)
bounds = (l_lims, u_lims)
import pyswarms.backend as P
self.P = P
# Global topology will always be used to compute gbest
from pyswarms.backend.topology import Star
self.global_topology = Star()
self.communication_topology = communication_topology
# Unless specified, use the star topology.
if self.communication_topology == 'random':
from pyswarms.backend.topology import Random
self.topology = Random() # The Topology Class
elif self.communication_topology == 'local':
from pyswarms.backend.topology import Ring
self.topology = Ring() # The Topology Class
else:
from pyswarms.backend.topology import Star
self.topology = Star() # The Topology Class
self.options = {'c1': self.c1, 'c2': self.c2, 'w': self.w}
self.swarm = self.P.create_swarm(n_particles=self.popsize,
dimensions=self.num_params,
options=self.options,
center=self.sigma_init,
bounds=bounds)
def ask(self):
'''returns a list of parameters'''
if self.first_iteration:
self.solutions = np.copy(self.swarm.position)
return self.pop_params
self.swarm.velocity = self.topology.compute_velocity(self.swarm)
self.swarm.position = self.topology.compute_position(self.swarm)
self.solutions = np.copy(self.swarm.position)
return self.solutions
def tell(self, reward_table_result):
# input must be a numpy float array
assert (len(reward_table_result) == self.popsize
), "Inconsistent reward_table size reported."
reward_table = -np.array(reward_table_result) # Maximize!
if self.weight_decay > 0:
l2_decay = compute_weight_decay(self.weight_decay, self.solutions)
reward_table += l2_decay
self.swarm.current_cost = reward_table #pyswarm
# If it's the first iteration, initialize pbest_cost, best_reward and
# best_param
if self.first_iteration:
self.first_iteration = False
self.swarm.pbest_cost = np.copy(self.swarm.current_cost)
self.best_reward = np.min(self.swarm.current_cost)
self.best_param = self.solutions[np.argmin(
self.swarm.current_cost)]
else:
self.swarm.pbest_pos, self.swarm.pbest_cost = self.P.compute_pbest(
self.swarm) # Update and store
# Update gbest.
if self.communication_topology == 'random':
self.swarm.best_pos, self.swarm.best_cost = self.topology.compute_gbest(
self.swarm, k=(self.popsize * 3) // 4) # k = 4 typically
elif self.communication_topology == 'local':
self.swarm.best_pos, self.swarm.best_cost = self.topology.compute_gbest(
self.swarm, p=2, k=2)
else: # star
self.swarm.best_pos, self.swarm.best_cost = self.topology.compute_gbest(
self.swarm)
# Update best_reward and best_param if pertinent.
if np.min(self.swarm.pbest_cost) < self.best_reward:
self.best_reward = np.min(self.swarm.pbest_cost)
self.best_param = self.solutions[np.argmin(self.swarm.pbest_cost)]
def rms_stdev(self):
return np.std(self.solutions)
def best_param(self):
return self.best_param
def current_param(self):
return self.solutions[np.argmin(self.swarm.current_cost)]
def result(
self
): # return best params so far, along with historically best reward, curr reward, sigma_init
return (self.best_param, -self.best_reward,
-np.min(self.swarm.current_cost), self.sigma_init)
