class MicropolisEnv(core.Env):
def __init__(self, MAP_X=20, MAP_Y=20, PADDING=0):
self.SHOW_GUI = False
self.start_time = time.time()
self.print_map = False
self.num_episode = 0
self.max_static = 0
self.player_step = False
self.static_player_builds = False
### MIXED
self.city_trgs = OrderedDict({
'res_pop': 500,
'com_pop': 50,
'ind_pop': 50,
'traffic': 2000,
# i believe one plant is worth 12, the other 16?
'num_plants': 14,
'mayor_rating': 100
})
self.trg_param_vals = np.array([v for v in self.city_trgs.values()])
self.param_bounds = OrderedDict({
'res_pop': (0, 750),
'com_pop': (0, 100),
'ind_pop': (0, 100),
'traffic': (0, 2000),
'num_plants': (0, 100),
'mayor_rating': (0, 100)
})
self.weights = OrderedDict({
'res_pop': 1,
'com_pop': 1,
'ind_pop': 1,
'traffic': 1,
'num_plants': 0,
'mayor_rating': 0,
})
self.num_params = 6
# not necessarily true but should take care of most cases
self.max_loss = 0
i = 0
self.param_ranges = []
for param, (lb, ub) in self.param_bounds.items():
weight = self.weights[param]
rng = abs(ub - lb)
self.param_ranges += [rng]
if i < self.num_params:
self.max_loss += rng * weight
i += 1
### MIXED
#self.city_trgs = {
# 'res_pop': 1,
# 'com_pop': 4,
# 'ind_pop': 4,
# 'traffic': 0.2,
# 'num_plants': 0,
# 'mayor_rating': 0}
### Traffic
#self.city_trgs = {
# 'res_pop': 1,
# 'com_pop': 4,
# 'ind_pop': 4,
# 'traffic': 5,
# 'num_plants': 0,
# 'mayor_rating':0
# }
self.city_metrics = {}
self.max_reward = 100
#self.setMapSize((MAP_X, MAP_Y), PADDING)
def seed(self, seed=None):
self.np_random, seed1 = seeding.np_random(seed)
# Derive a random seed. This gets passed as a uint, but gets
# checked as an int elsewhere, so we need to keep it below
# 2**31.
seed2 = seeding.hash_seed(seed1 + 1) % 2**31
np.random.seed(seed)
return [seed1, seed2]
def setMapSize(self, size, **kwargs):
'''Do most of the actual initialization.
'''
self.pre_gui(size, **kwargs)
#TODO: this better
if hasattr(self, 'micro'):
self.micro.reset_params(size)
else:
self.micro = MicropolisControl(self,
self.MAP_X,
self.MAP_Y,
self.PADDING,
rank=self.rank,
power_puzzle=self.power_puzzle,
gui=self.render_gui)
self.city_metrics = self.get_city_metrics()
self.last_city_metrics = self.city_metrics
self.post_gui()
def pre_gui(self,
size,
max_step=None,
rank=0,
print_map=False,
PADDING=0,
static_builds=True,
parallel_gui=False,
render_gui=False,
empty_start=True,
simple_reward=False,
power_puzzle=False,
record=False,
traffic_only=False,
random_builds=False,
poet=False,
**kwargs):
self.PADDING = PADDING
self.rank = rank
self.render_gui = render_gui
self.random_builds = random_builds
self.traffic_only = traffic_only
if record: raise NotImplementedError
if max_step is None:
max_step = size * size
self.max_step = max_step
self.empty_start = empty_start
self.simple_reward = simple_reward
self.power_puzzle = power_puzzle
if type(size) == int:
self.MAP_X = size
self.MAP_Y = size
else:
self.MAP_X = size[0]
self.MAP_Y = size[1]
self.obs_width = self.MAP_X + PADDING * 2
self.static_builds = True
self.poet = poet
self.print_map = print_map
def post_gui(self):
self.win1 = self.micro.win1
self.micro.SHOW_GUI = self.SHOW_GUI
self.num_step = 0
self.minFunds = 0
self.initFunds = self.micro.init_funds
self.num_tools = self.micro.num_tools
self.num_zones = self.micro.num_zones
# res, com, ind pop, demand
self.num_scalars = 6
self.num_density_maps = 3
num_user_features = 1 # static builds
# traffic, power, density
print('num map features: {}'.format(self.micro.map.num_features))
self.num_obs_channels = self.micro.map.num_features + self.num_scalars \
+ self.num_density_maps + num_user_features
if self.poet:
self.num_obs_channels += len(self.city_trgs)
#ac_low = np.zeros((3))
#ac_high = np.array([self.num_tools - 1, self.MAP_X - 1, self.MAP_Y - 1])
#self.action_space = spaces.Box(low=ac_low, high=ac_high, dtype=int)
self.action_space = spaces.Discrete(self.num_tools * self.MAP_X *
self.MAP_Y)
self.last_state = None
self.metadata = {'runtime.vectorized': True}
low_obs = np.full((self.num_obs_channels, self.MAP_X, self.MAP_Y),
fill_value=-1)
high_obs = np.full((self.num_obs_channels, self.MAP_X, self.MAP_Y),
fill_value=1)
self.observation_space = spaces.Box(low=low_obs,
high=high_obs,
dtype=float)
self.state = None
self.intsToActions = {}
self.actionsToInts = np.zeros((self.num_tools, self.MAP_X, self.MAP_Y))
self.mapIntsToActions()
self.last_pop = 0
self.last_num_roads = 0
# self.past_actions = np.full((self.num_tools, self.MAP_X, self.MAP_Y), False)
self.auto_reset = True
self.mayor_rating = 50
self.last_mayor_rating = self.mayor_rating
self.last_priority_road_net_size = 0
self.display_city_trgs()
if self.render_gui and self.rank == 0:
self.render()
def get_param_bounds(self):
return self.param_bounds
def display_city_trgs(self):
if self.win1 is not None:
self.win1.agentPanel.displayTrgs(self.city_trgs)
return self.city_trgs
def mapIntsToActionsChunk(self):
''' Unrolls the action vector into spatial chunks (does this matter empirically?).'''
w0 = 20
w1 = 10
i = 0
for j0 in range(self.MAP_X // w0):
for k0 in range(self.MAP_Y // w0):
for j1 in range(w0 // w1):
for k1 in range(w0 // w1):
for z in range(self.num_tools):
for x in range(j0 * w0 + j1 * w1,
j0 * w0 + (j1 + 1) * w1):
for y in range(k0 * w0 + k1 * w1,
k0 * w0 + (k1 + 1) * w1):
self.intsToActions[i] = [z, x, y]
i += 1
def mapIntsToActions(self):
''' Unrolls the action vector in the same order as the pytorch model
on its forward pass.'''
chunk_width = 1
i = 0
for z in range(self.num_tools):
for x in range(self.MAP_X):
for y in range(self.MAP_Y):
self.intsToActions[i] = [z, x, y]
self.actionsToInts[z, x, y] = i
i += 1
print('len of intsToActions: {}\n num tools: {}'.format(
len(self.intsToActions), self.num_tools))
def randomStep(self):
self.step(self.action_space.sample())
def close(self):
self.micro.close()
def randomStaticStart(self):
num_static = self.MAP_X * self.MAP_Y / 10
lst_epi = 500
# num_static = math.ceil(((lst_epi - self.num_episode) / lst_epi) * num_static)
# num_static = max(0, max_static)
self.micro.setFunds(self.micro.init_funds)
if num_static > 0:
num_static = self.np_random.randint(0, num_static + 1)
for i in range(num_static):
if i % 2 == 0:
static_build = True
else:
static_build = False
self.step(self.action_space.sample(), static_build=True)
def randomStart(self):
r = self.np_random.randint(0, 100)
self.micro.setFunds(self.micro.init_funds)
for i in range(r):
self.step(self.action_space.sample())
# i = np.random.randint(0, (self.obs_width * self.obs_width / 3))
# a = (np.random.randint(0, self.num_tools, i), np.random.randint(0, self.obs_width, i), np.random.randint(0, self.obs_width, i))
# for j in range(i):
# self.micro.takeSetupAction((a[0][j], a[1][j], a[2][j]))
def powerPuzzle(self):
''' Set up one plant, one res. If we restrict the agent to building power lines, we can test its ability
to make long-range associations. '''
for i in range(5):
self.micro.doBotTool(np.random.randint(0, self.micro.MAP_X),
np.random.randint(0, self.micro.MAP_Y),
'Residential',
static_build=True)
while self.micro.map.num_plants == 0:
self.micro.doBotTool(np.random.randint(0, self.micro.MAP_X),
np.random.randint(0, self.micro.MAP_Y),
'NuclearPowerPlant',
static_build=True)
def reset(self):
self.display_city_trgs()
if True:
#if self.render_gui:
if False:
self.micro.clearBotBuilds()
else:
self.micro.clearMap()
if not self.empty_start:
self.micro.newMap()
self.num_step = 0
if self.power_puzzle:
self.powerPuzzle()
if self.random_builds:
self.randomStaticStart()
self.micro.simTick()
self.city_metrics = self.get_city_metrics()
self.last_city_metrics = self.city_metrics
self.micro.setFunds(self.micro.init_funds)
#curr_funds = self.micro.getFunds()
self.curr_pop = 0
self.curr_reward = self.getReward()
self.state = self.getState()
self.last_pop = 0
self.micro.num_roads = 0
self.last_num_roads = 0
#self.past_actions.fill(False)
self.num_episode += 1
return self.state
# def getRoadPenalty(self):
#
# class roadPenalty(torch.nn.module):
# def __init__(self):
# super(roadPenalty, self).__init__()
# self.
def getState(self):
res_pop, com_pop, ind_pop = self.micro.getResPop(
), self.micro.getComPop(), self.micro.getIndPop()
resDemand, comDemand, indDemand = self.micro.engine.getDemands()
scalars = [res_pop, com_pop, ind_pop, resDemand, comDemand, indDemand]
if self.poet:
for j in range(3):
scalars[j] = scalars[j] / self.param_ranges[j]
trg_metrics = [v for k, v in self.city_trgs.items()]
for i in range(len(trg_metrics)):
trg_metrics[i] = trg_metrics[i] / self.param_ranges[i]
scalars += trg_metrics
return self.observation(scalars)
def observation(self, scalars):
state = self.micro.map.getMapState()
density_maps = self.micro.getDensityMaps()
#if self.render_gui:
# print(density_maps[2])
road_networks = self.micro.map.road_networks
if self.render_gui:
#print(road_networks, self.micro.map.road_net_sizes)
pass
scalar_layers = np.zeros((len(scalars), self.MAP_X, self.MAP_Y))
for si in range(len(scalars)):
fill_val = scalars[si]
if not type(fill_val) == str:
scalar_layers[si].fill(scalars[si])
state = np.concatenate((state, density_maps, scalar_layers), 0)
if self.static_builds:
state = np.concatenate((state, self.micro.map.static_builds), 0)
return state
def getPop(self):
self.resPop, self.comPop, self.indPop = self.micro.getResPop(), \
self.micro.getComPop(), \
self.micro.getIndPop()
curr_pop = self.resPop + \
self.comPop + \
self.indPop
return curr_pop
def getReward(self):
'''Calculate reward.
'''
if True:
reward = 0
for metric, trg in self.city_trgs.items():
last_val = self.last_city_metrics[metric]
trg_change = trg - last_val
val = self.city_metrics[metric]
change = val - last_val
if np.sign(change) != np.sign(trg_change):
metric_rew = -abs(change)
elif abs(change) < abs(trg_change):
metric_rew = abs(change)
else:
metric_rew = abs(trg_change) - abs(trg_change - change)
reward += metric_rew * self.weights[metric]
#if self.render_gui and reward != 0:
# print(self.city_metrics)
# print(self.city_trgs)
# print(reward)
# print()
#if False:
# max_reward = self.max_reward
# loss = 0
# i = 0
# for k, v in self.city_trgs.items():
# if i == self.num_params:
# break
# else:
# if True:
# reward = 0
# for metric_name, trg in self.city_trgs.items():
# weight = self.weights[k]
# loss += abs(v - self.city_metrics[k]) * weight
# i += 1
# reward = (self.max_loss - loss) * max_reward / self.max_loss
# reward = self.getPopReward()
#self.curr_reward = reward
return reward
def getPopReward(self):
if False:
pop_reward = self.micro.getTotPop()
else:
resPop, comPop, indPop = (1 / 4) * self.micro.getResPop(
), self.micro.getComPop(), self.micro.getIndPop()
pop_reward = resPop + comPop + indPop
# population density per 16x16 section of map
pop_reward = pop_reward / (self.MAP_X * self.MAP_Y / 16**2)
zone_variety = 0
if resPop > 0:
zone_variety += 1
if comPop > 0:
zone_variety += 1
if indPop > 0:
zone_variety += 1
zone_bonus = (zone_variety - 1) * 50
pop_reward += max(0, zone_bonus)
if False:
pop_reward = (resPop + 1) * (comPop + 1) * (indPop + 1) - 1
return 0
return pop_reward
def set_param_bounds(self, bounds):
print('setting visual param bounds (TODO: forreal')
if self.win1:
self.win1.agentPanel.setMetricRanges(bounds)
def set_params(self, trgs):
for k, v in trgs.items():
self.city_trgs[k] = v
self.trg_param_vals = np.array([v for v in self.city_trgs.values()])
self.display_city_trgs()
#print('set city trgs of env {} to: {}'.format(self.rank, self.city_trgs))
def get_city_metrics(self):
res_pop, com_pop, ind_pop = self.micro.getResPop(), \
self.micro.getComPop(), \
self.micro.getIndPop()
traffic = self.micro.total_traffic
mayor_rating = self.getRating()
num_plants = self.micro.map.num_plants
city_metrics = {
'res_pop': res_pop,
'com_pop': com_pop,
'ind_pop': ind_pop,
'traffic': traffic,
'num_plants': num_plants,
'mayor_rating': mayor_rating
}
return city_metrics
def display_city_metrics(self):
if self.win1 is not None:
self.win1.agentPanel.displayMetrics(self.city_metrics)
def step(self, a, static_build=False):
#self.micro.engine.setPasses(np.random.randint(1, 101))
if self.player_step:
#if self.player_step == a:
# static_build=False
#static_build = True
if self.static_player_builds:
static_build = True
a = self.player_step
self.player_step = False
#else:
# a = 0
a = self.intsToActions[a]
self.micro.takeAction(a, static_build)
return self.postact()
def postact(self):
# never let the agent go broke, for now
self.micro.setFunds(self.micro.init_funds)
#print('rank {} tickin'.format(self.rank))
# TODO: BROKEN!
self.micro.simTick()
self.state = self.getState()
#print(self.state[-2])
self.curr_pop = self.getPop()
self.last_city_metrics = self.city_metrics
self.city_metrics = self.get_city_metrics()
if self.render_gui:
self.display_city_metrics()
#if self.traffic_only:
# self.curr_pop = self.getPopReward() / 1
# #self.curr_pop = 0
#else:
# self.curr_pop = self.getPop() #** 2
# #self.curr_pop = self.getPopReward() #** 2
#pop_reward = self.curr_pop
#self.curr_mayor_rating = self.getRating()
#if not self.simple_reward:
# #if self.micro.total_traffic > 0:
# # print(self.micro.total_traffic)
# if self.traffic_only:
# traffic_reward = self.micro.total_traffic * 10
# #traffic_reward = 0
# else:
# #traffic_reward = self.micro.total_traffic / 100
# traffic_reward = self.reward_weights[3] * self.micro.total_traffic
# if self.player_step:
# print('pop reward: {}\n'
# 'traffic reward: {}'.format(pop_reward, traffic_reward))
# self.player_step = None
# if pop_reward > 0 and traffic_reward > 0:
# #print(pop_reward, traffic_reward)
# pass
# reward = pop_reward + traffic_reward
# if reward > 0 and self.micro.map.num_roads > 0 and not self.traffic_only: # to avoid one-road minima in early training
# max_net_1 = 0
# max_net_2 = 0
# for n in self.micro.map.road_net_sizes.values():
# if n > max_net_1:
# max_net_1 = n
# # max_net_2 = max_net_1
# #elif n > max_net_2:
# # max_net_2 = n
reward = 0
reward = self.getReward()
#reward = reward / (self.max_step)
self.curr_funds = curr_funds = self.micro.getFunds()
bankrupt = curr_funds < self.minFunds
terminal = (bankrupt or self.num_step >= self.max_step) and\
self.auto_reset
if self.print_map:
#if static_build:
# print('STATIC BUILD')
self.printMap()
if self.render_gui:
#pass
self.micro.render()
infos = {}
# Get the next player-build ready, if there is one in the queue
if self.micro.player_builds:
b = self.micro.player_builds[0]
a = self.actionsToInts[b]
infos['player_move'] = int(a)
self.micro.player_builds = self.micro.player_builds[1:]
self.player_step = a
self.num_step += 1
## Override Reward
#reward = self.city_metrics['res_pop'] + self.city_metrics['com_pop']\
# + self.city_metrics['ind_pop'] + self.city_metrics['traffic']
return (self.state, reward, terminal, infos)
def getRating(self):
return self.micro.engine.cityYes
def printMap(self, static_builds=True):
#if static_builds:
# static_map = self.micro.map.static_builds
#else:
# static_map = None
np.set_printoptions(threshold=np.inf)
zone_map = self.micro.map.zoneMap[-1]
zone_map = zone_map.transpose(1, 0)
zone_map = np.array_repr(zone_map).replace(', ', ' ').replace(
'],\n', ']\n').replace(',\n', ',').replace(', ', ' ').replace(
' ', ' ').replace(' ', ' ')
print(
'{} \n population: {}, traffic: {}, episode: {}, step: {}, reward: {} \n'
.format(
zone_map,
self.curr_pop,
self.micro.total_traffic,
self.num_episode,
self.num_step,
self.curr_reward #, static_map
))
#print(self.micro.map.centers)
def render(self, mode='human'):
self.micro.render()
def test(self):
env = MicropolisEnv()
for i in range(5000):
env.step(env.action_space.sample())
def set_res_weight(self, val):
self.city_trgs['res_pop'] = val
def set_com_weight(self, val):
self.city_trgs['com_pop'] = val
def set_ind_weight(self, val):
self.city_trgs['ind_pop'] = val
def set_traffic_weight(self, val):
self.city_trgs['traffic'] = val
def set_plants_weight(self, val):
self.city_trgs['num_plants'] = val
def set_rating_weight(self, val):
self.city_trgs['mayor_rating'] = val
class MicropolisEnv(core.Env):
def __init__(self, MAP_X=20, MAP_Y=20, PADDING=0):
self.SHOW_GUI=False
self.start_time = time.time()
self.print_map = False
self.num_episode = 0
self.max_static = 0
self.player_step = False
self.last_reward = 0
#self.setMapSize((MAP_X, MAP_Y), PADDING)
def seed(self, seed=None):
self.np_random, seed1 = seeding.np_random(seed)
# Derive a random seed. This gets passed as a uint, but gets
# checked as an int elsewhere, so we need to keep it below
# 2**31.
seed2 = seeding.hash_seed(seed1 + 1) % 2**31
np.random.seed(seed)
return [seed1, seed2]
def setMapSize(self, size, max_step=None, rank=None, print_map=False,
PADDING=0, static_builds=True, parallel_gui=False,
render_gui=False, empty_start=True, simple_reward=False,
power_puzzle=False, record=False):
if record: raise NotImplementedError
if max_step is not None:
self.max_step = max_step
else:
self.max_step = 1000
self.empty_start = empty_start
self.simple_reward = simple_reward
self.power_puzzle = power_puzzle
if type(size) == int:
self.MAP_X = size
self.MAP_Y = size
else:
self.MAP_X = size[0]
self.MAP_Y = size[1]
self.obs_width = self.MAP_X + PADDING * 2
self.micro = MicropolisControl(self.MAP_X, self.MAP_Y, PADDING, parallel_gui=parallel_gui, rank=rank,
power_puzzle=power_puzzle)
self.static_builds = True
self.win1 = self.micro.win1
self.micro.SHOW_GUI=self.SHOW_GUI
self.num_step = 0
self.minFunds = 5000
self.initFunds = 10000000
self.num_tools = self.micro.num_tools
self.num_zones = self.micro.num_zones
# res, com, ind pop, demand
self.num_scalars = 6
self.num_density_maps = 3
num_user_features = 1 # static builds
# traffic, power, density
self.num_obs_channels = self.micro.map.num_features + self.num_scalars + self.num_density_maps + num_user_features
#ac_low = np.zeros((3))
#ac_high = np.array([self.num_tools - 1, self.MAP_X - 1, self.MAP_Y - 1])
#self.action_space = spaces.Box(low=ac_low, high=ac_high, dtype=int)
self.action_space = spaces.Discrete(self.num_tools * self.MAP_X * self.MAP_Y)
self.last_state = None
self.metadata = {'runtime.vectorized': True}
low_obs = np.full((self.num_obs_channels, self.MAP_X, self.MAP_Y), fill_value=-1)
high_obs = np.full((self.num_obs_channels, self.MAP_X, self.MAP_Y), fill_value=1)
# TODO: can/should we use Tuples of MultiBinaries instead, for greater efficiency?
self.observation_space = spaces.Box(low=low_obs, high=high_obs, dtype = float)
self.state = None
self.intsToActions = {}
self.actionsToInts = np.zeros((self.num_tools, self.MAP_X, self.MAP_Y))
self.mapIntsToActions()
self.last_pop = 0
self.last_num_roads = 0
# self.past_actions = np.full((self.num_tools, self.MAP_X, self.MAP_Y), False)
self.print_map = print_map
self.render_gui = render_gui
self.mayor_rating = 50
self.last_mayor_rating = self.mayor_rating
self.last_priority_road_net_size = 0
def mapIntsToActionsChunk(self):
''' Unrolls the action vector into spatial chunks (does this matter empirically?).'''
w0 = 20
w1 = 10
i = 0
for j0 in range(self.MAP_X // w0):
for k0 in range(self.MAP_Y // w0):
for j1 in range(w0 // w1):
for k1 in range(w0 // w1):
for z in range(self.num_tools):
for x in range(j0 * w0 + j1*w1,
j0 * w0 + (j1+1)*w1):
for y in range(k0 * w0 + k1*w1,
k0 * w0 + (k1+1)*w1):
self.intsToActions[i] = [z, x, y]
i += 1
def mapIntsToActions(self):
''' Unrolls the action vector in the same order as the pytorch model
on its forward pass.'''
chunk_width = 1
i = 0
for z in range(self.num_tools):
for x in range(self.MAP_X):
for y in range(self.MAP_Y):
self.intsToActions[i] = [z, x, y]
self.actionsToInts[z, x, y] = i
i += 1
print('len of intsToActions: {}\n num tools: {}'.format(len(self.intsToActions), self.num_tools))
def randomStep(self):
self.step(self.action_space.sample())
def close(self):
self.micro.close()
def randomStaticStart(self):
num_static = 100
lst_epi = 500
# num_static = math.ceil(((lst_epi - self.num_episode) / lst_epi) * num_static)
# num_static = max(0, max_static)
self.micro.setFunds(10000000)
if num_static > 0:
num_static = self.np_random.randint(0, num_static + 1)
for i in range(num_static):
if i % 2 == 0:
static_build = True
else:
static_build = False
self.step(self.action_space.sample(), static_build=True)
def randomStart(self):
r = self.np_random.randint(0, 100)
self.micro.setFunds(10000000)
for i in range(r):
self.step(self.action_space.sample())
# i = np.random.randint(0, (self.obs_width * self.obs_width / 3))
# a = (np.random.randint(0, self.num_tools, i), np.random.randint(0, self.obs_width, i), np.random.randint(0, self.obs_width, i))
# for j in range(i):
# self.micro.takeSetupAction((a[0][j], a[1][j], a[2][j]))
def powerPuzzle(self):
''' Set up one plant, one res. If we restrict the agent to building power lines, we can test its ability
to make long-range associations. '''
for i in range(5):
self.micro.doBotTool(np.random.randint(0, self.micro.MAP_X),
np.random.randint(0, self.micro.MAP_Y), 'Residential', static_build=True)
while self.micro.map.num_plants == 0:
self.micro.doBotTool(np.random.randint(0, self.micro.MAP_X),
np.random.randint(0, self.micro.MAP_Y),
'NuclearPowerPlant', static_build=True)
def reset(self):
if True:
#if self.render_gui:
if False:
self.micro.clearBotBuilds()
else:
self.micro.clearMap()
if not self.empty_start:
self.micro.newMap()
self.num_step = 0
if self.power_puzzle:
self.powerPuzzle()
#self.randomStaticStart()
self.micro.engine.simTick()
self.micro.setFunds(self.initFunds)
#curr_funds = self.micro.getFunds()
curr_pop = self.getPop()
self.state = self.getState()
self.last_pop=0
self.micro.num_roads = 0
self.last_num_roads = 0
#self.past_actions.fill(False)
self.num_episode += 1
self.curr_reward = 0
self.last_reward = 0
return self.state
# def getRoadPenalty(self):
#
# class roadPenalty(torch.nn.module):
# def __init__(self):
# super(roadPenalty, self).__init__()
# self.
def getState(self):
resPop, comPop, indPop = self.micro.getResPop(), self.micro.getComPop(), self.micro.getIndPop()
resDemand, comDemand, indDemand = self.micro.engine.getDemands()
scalars = [resPop, comPop, indPop, resDemand, comDemand, indDemand]
return self.observation(scalars)
def observation(self, scalars):
state = self.micro.map.getMapState()
density_maps = self.micro.getDensityMaps()
road_networks = self.micro.map.road_networks
if self.render_gui:
#print(road_networks, self.micro.map.road_net_sizes)
pass
scalar_layers = np.zeros((len(scalars), self.MAP_X, self.MAP_Y))
for si in range(len(scalars)):
scalar_layers[si].fill(scalars[si])
state = np.concatenate((state, density_maps, scalar_layers), 0)
if self.static_builds:
state = np.concatenate((state, self.micro.map.static_builds), 0)
return state
def getPop(self):
resPop, comPop, indPop = (1/4) * self.micro.getResPop(), \
self.micro.getComPop(), \
self.micro.getIndPop()
curr_pop = resPop + \
comPop + \
indPop
return curr_pop
def getPopReward(self):
if self.simple_reward:
return self.micro.getTotPop()
else:
resPop, comPop, indPop = (1/4) * self.micro.getResPop(), self.micro.getComPop(), self.micro.getIndPop()
curr_pop = resPop + comPop + indPop
zone_variety = 0
if resPop > 0:
zone_variety += 1
if comPop > 0:
zone_variety += 1
if indPop > 0:
zone_variety += 1
zone_bonus = (zone_variety - 1) * 50
curr_pop += max(0, zone_bonus)
return curr_pop
def step(self, a, static_build=False):
#self.micro.engine.setPasses(np.random.randint(1, 101))
if self.player_step:
if self.player_step == a:
static_build=False
self.player_step = None
a = self.intsToActions[a]
self.micro.takeAction(a, static_build)
reward = 0
self.curr_pop = self.getPopReward() ** 2
self.curr_mayor_rating = self.getRating()
self.state = self.getState()
if not self.simple_reward:
reward = self.curr_pop + (self.micro.total_traffic / 100)
if reward > 0 and self.micro.map.num_roads > 0: # to avoid one-road minima in early training
max_net_1 = 0
max_net_2 = 0
for n in self.micro.map.road_net_sizes.values():
if n > max_net_1:
max_net_1 = n
max_net_2 = max_net_1
elif n > max_net_2:
max_net_2 = n
reward += (min(max_net_1, max_net_2) / self.micro.map.num_roads) * min(100, reward) # the avg reward when roads are introduced to boost res, so
# proportion of max net to tal roads *
reward -= min((max(1, self.micro.map.num_plants) - 1) * 1,
self.curr_pop / 2)
else:
reward = self.curr_pop
reward = reward / self.max_step
self.curr_reward = reward#- self.last_reward
self.last_reward = reward
#reward += (self.curr_mayor_rating - self.last_mayor_rating)
self.last_mayor_rating = self.curr_mayor_rating
self.last_pop = self.curr_pop
curr_funds = self.micro.getFunds()
bankrupt = curr_funds < self.minFunds
terminal = bankrupt or self.num_step >= self.max_step
if True and self.print_map:
#if static_build:
# print('STATIC BUILD')
self.printMap()
if self.render_gui:
#pass
self.micro.render()
infos = {}
if self.micro.player_builds:
b = self.micro.player_builds[0]
a = self.actionsToInts[b]
infos['player_move'] = int(a)
self.micro.player_builds = self.micro.player_builds[1:]
self.player_step = a
self.num_step += 1
return (self.state, self.curr_reward, terminal, infos)
def getRating(self):
return self.micro.engine.cityYes
def printMap(self, static_builds=True):
#if static_builds:
# static_map = self.micro.map.static_builds
#else:
# static_map = None
np.set_printoptions(threshold=np.inf)
zone_map = self.micro.map.zoneMap[-1]
zone_map = np.array_repr(zone_map).replace(', ',' ').replace('],\n', ']\n').replace(',\n', ',').replace(', ', ' ').replace(' ',' ').replace(' ',' ')
print('{}\npopulation: {}, traffic: {}, episode: {}, step: {}, reward: {}'.format(zone_map, self.curr_pop, self.micro.total_traffic, self.num_episode, self.num_step, self.curr_reward#, static_map
))
#print(self.micro.map.centers)
def render(self, mode='human'):
self.micro.render()
def test(self):
env = MicropolisEnv()
for i in range(5000):
env.step(env.action_space.sample())