def add_preys(self, num):
self.increase_preys += num
ind = np.where(self.map == 0)
perm = np.random.permutation(np.arange(len(ind[0])))
for i in range(num):
agent = Agent()
health = np.random.uniform(self.min_health, self.max_health)
agent.health = health
agent.birth_time = self.timestep
agent.predator = False
agent.life = np.random.normal(500, 100)
agent.attack = np.random.uniform(self.min_attack, self.max_attack)
agent.resilience = np.random.uniform(self.min_resilience,
self.max_resilience)
agent.gene_attack = agent.attack
agent.gene_resilience = agent.resilience
agent.speed = np.random.randint(self.min_speed, self.max_speed)
agent.gene_speed = agent.speed
agent.id = self.max_id
self.max_id += 1
agent.property = [self._gen_power(agent.id), [1, 0, 0]]
x = ind[0][perm[i]]
y = ind[1][perm[i]]
if self.map[x][y] == 0:
self.map[x][y] = agent.id
self.large_map[
x:self.large_map.shape[0]:self.map.shape[0],
y:self.large_map.shape[1]:self.map.shape[1]] = agent.id
agent.pos = (x, y)
self.preys[agent.id] = agent
def add_predators(self, num):
'''
Add predators
Args:
num: Number of predator this function generates
'''
self.increase_predators += num
ind = np.where(self.map == 0)
perm = np.random.permutation(np.arange(len(ind[0])))
if self.experiment_type == 'variation':
total = len(self.random_predators) + len(
self.trained_predators) + len(self.training_predators)
p = [
len(self.random_predators) / total,
len(self.trained_predators) / total,
len(self.training_predators) / total
]
for i in range(num):
agent = Agent()
agent.health = 1
agent.original_health = 1
agent.birth_time = self.timestep
agent.predator = True
agent.id = self.max_id
self.max_id += 1
agent.speed = 1
agent.life = np.random.normal(500, 100)
agent.hunt_square = self.max_hunt_square
agent.property = [self._gen_power(agent.id), [0, 0, 1]]
x = ind[0][perm[i]]
y = ind[1][perm[i]]
if self.map[x][y] == 0:
self.map[x][y] = agent.id
self.large_map[
x:self.large_map.shape[0]:self.map.shape[0],
y:self.large_map.shape[1]:self.map.shape[1]] = agent.id
agent.pos = (x, y)
self.predator_num += 1
if self.experiment_type == 'variation':
exp_type = np.random.choice(3, p=p)
if exp_type == 0:
agent.policy_type = 'random'
self.random_predators[agent.id] = agent
elif exp_type == 1:
agent.policy_type = 'trained'
self.trained_predators[agent.id] = agent
else:
agent.policy_type = 'training'
self.training_predators[agent.id] = agent
else:
self.predators[agent.id] = agent
def increase_predator(self, prob):
'''
Generates new predators
Args:
prob: Ratio against the population which determins how many new agents generated.
'''
num = max(1, int(self.predator_num * prob))
self.increase_predators = num
ind = np.where(self.map == 0)
perm = np.random.permutation(np.arange(len(ind[0])))
if self.experiment_type == 'variation':
total = len(self.random_predators) + len(
self.trained_predators) + len(self.training_predators)
p = [
len(self.random_predators) / total,
len(self.trained_predators) / total,
len(self.training_predators) / total
]
for i in range(num):
agent = Agent()
agent.health = np.random.uniform(self.min_health, self.max_health)
agent.original_health = agent.health
agent.birth_time = self.timestep
agent.predator = True
agent.id = self.max_id
self.max_id += 1
agent.speed = 1
agent.hunt_square = self.max_hunt_square
agent.property = [self._gen_power(agent.id), [0, 0, 1]]
x = ind[0][perm[i]]
y = ind[1][perm[i]]
if self.map[x][y] == 0:
self.map[x][y] = agent.id
self.large_map[
x:self.large_map.shape[0]:self.map.shape[0],
y:self.large_map.shape[1]:self.map.shape[1]] = agent.id
agent.pos = (x, y)
if self.experiment_type == 'variation':
exp_type = np.random.choice(3, p=p)
if exp_type == 0:
agent.policy_type = 'random'
self.random_predators[agent.id] = agent
elif exp_type == 1:
agent.policy_type = 'trained'
self.trained_predators[agent.id] = agent
else:
agent.policy_type = 'training'
self.training_predators[agent.id] = agent
else:
self.predators[agent.id] = agent
self.predator_num += 1
def crossover_prey(self, crossover_scope=3, crossover_rate=0.001):
'''
Mating function which generates new agents with the given probability if agents are within a certain square
Args:
crossover_scope: Scope of crossover. If two agents are within this scope, then those agents are candidates for the crossover
crossover_rate: The probability that two agents successfully reproduce a new agent
'''
ind = np.where(self.map == 0)
perm = np.random.permutation(np.arange(len(ind[0])))
index = 0
for prey in list(self.prey_agents.values()):
x, y = prey.pos
local_map = self.large_map[(self.w + x - crossover_scope // 2):(
self.w + x - crossover_scope // 2 +
crossover_scope), (self.h + y - crossover_scope // 2):(
self.h + y - crossover_scope // 2 + crossover_scope)]
agent_indices = np.where(local_map > 0)
flag = True
if len(agent_indices[0]) == 0 or prey.crossover:
continue
for candidate_x, candidate_y in zip(agent_indices[0],
agent_indices[1]):
candidate_id = local_map[candidate_x, candidate_y]
candidate_agent = self.agents[candidate_id]
prey.checked.append(candidate_agent.id)
if (not candidate_agent.predator and not candidate_agent.crossover and candidate_agent.id != prey.id and \
prey.id not in candidate_agent.checked and prey.age > self.args.min_crossover_age and candidate_agent.age > self.args.min_crossover_age and len(self.prey_agents) <= self.args.prey_capacity):
candidate_agent.get_closer = True
if np.random.rand() < crossover_rate and flag:
for i in range(
np.random.randint(
self.args.max_prey_offsprings) + 1):
candidate_agent.crossover = True
prey.crossover = True
child = Agent()
child.id = self.max_id
self.max_id += 1
child.predator = False
child.life = np.random.normal(500, scale=100)
child.health = 1
new_embedding = np.random.normal(
size=[self.agent_emb_dim])
self.agent_embeddings[child.id] = new_embedding
child.hunt_square = self.max_hunt_square
child.property = [
self._gen_power(child.id), [1, 0, 0]
]
new_pos_indices = np.where(local_map == 0)
x = ind[0][perm[index]]
y = ind[1][perm[index]]
index += 1
self.map[x][y] = child.id
self.large_map[
x:self.large_map.shape[0]:self.map.shape[0],
y:self.large_map.shape[1]:self.map.
shape[1]] = child.id
child.pos = (x, y)
self.prey_num += 1
if self.experiment_type == 'variation':
rand = np.random.rand()
if rand < 0.5:
child.policy_type = prey.policy_type
else:
child.policy_type = candidate_agent.policy_type
if child.policy_type == 'random':
self.random_preys[child.id] = child
elif child.policy_type == 'trained':
self.trained_preys[child.id] = child
else:
self.training_preys[child.id] = child
else:
self.preys[child.id] = child
#candidate_agent.health -= 0.1
#prey.health -= 0.1
self.increase_preys += 1
flag = False
def crossover_prey(self,
crossover_scope=3,
crossover_rate=0.001,
mutation_prob=0.001):
ind = np.where(self.map == 0)
perm = np.random.permutation(np.arange(len(ind[0])))
index = 0
for prey in list(self.preys.values()):
x, y = prey.pos
local_map = self.large_map[(self.w + x - crossover_scope // 2):(
self.w + x - crossover_scope // 2 +
crossover_scope), (self.h + y - crossover_scope // 2):(
self.h + y - crossover_scope // 2 + crossover_scope)]
agent_indices = np.where(local_map > 0)
flag = True
if len(agent_indices[0]) == 0 or prey.crossover:
continue
for candidate_x, candidate_y in zip(agent_indices[0],
agent_indices[1]):
candidate_id = local_map[candidate_x, candidate_y]
candidate_agent = self.agents[candidate_id]
#prey.checked.append(candidate_agent.id)
if (not candidate_agent.predator and not candidate_agent.crossover and candidate_agent.id != prey.id and \
prey.id not in candidate_agent.checked and prey.age > self.args.min_crossover_age and candidate_agent.age > self.args.min_crossover_age \
and len(self.preys) <= self.args.prey_capacity):
candidate_agent.get_closer = True
if np.random.rand() < crossover_rate and flag:
#for i in range(np.random.randint(self.args.max_prey_offsprings)+1):
child = Agent()
child.id = self.max_id
self.max_id += 1
child.predator = False
child.life = np.random.normal(500, scale=100)
child.health = 1
rate = np.random.rand()
if np.random.rand() < mutation_prob:
child.attack = (
rate * prey.gene_attack +
(1 - rate) * candidate_agent.gene_attack
) + np.random.normal()
else:
child.attack = (
rate * prey.gene_attack +
(1 - rate) * candidate_agent.gene_attack)
rate = np.random.rand()
if np.random.rand() < mutation_prob:
child.resilience = (
rate * prey.gene_resilience +
(1 - rate) * candidate_agent.gene_resilience
) + np.random.normal()
else:
child.resilience = (
rate * prey.gene_resilience +
(1 - rate) * candidate_agent.gene_resilience)
rate = np.random.rand()
if np.random.rand() < mutation_prob:
speed = (rate * prey.gene_speed +
(1 - rate) * candidate_agent.gene_speed
) + np.random.normal()
speed = np.clip(speed, self.min_speed,
self.max_speed)
child.speed = int(speed)
else:
child.speed = int(
np.round(rate * prey.gene_speed + (1 - rate) *
candidate_agent.gene_speed))
child.gene_attack = child.attack
child.gene_resilience = child.resilience
child.gene_speed = child.speed
prey.reward = child.gene_attack + child.gene_resilience
candidate_agent.reward = child.gene_attack + child.gene_resilience
new_embedding = np.random.normal(
size=[self.agent_emb_dim])
self.agent_embeddings[child.id] = new_embedding
child.hunt_square = self.max_hunt_square
child.property = [self._gen_power(child.id), [1, 0, 0]]
x = ind[0][perm[index]]
y = ind[1][perm[index]]
index += 1
self.map[x][y] = child.id
self.large_map[
x:self.large_map.shape[0]:self.map.shape[0],
y:self.large_map.shape[1]:self.map.
shape[1]] = child.id
child.pos = (x, y)
self.preys[child.id] = child
self.prey_num += 1
#candidate_agent.health -= 0.1
#prey.health -= 0.1
self.increase_preys += 1
flag = False
candidate_agent.crossover = True
prey.crossover = True
def crossover_prey(self, prob, mutation_prob=0.001):
'''
Crossover function for preys
Args:
prob: Ratio against the population. This determins how many agents are chosen for the crossover
mutation_prob: Mutation probability
'''
num = max(1, int(self.prey_num * prob))
self.increase_preys = num
ind = np.where(self.map == 0)
perm = np.random.permutation(np.arange(len(ind[0])))
index = 0
preys = list(self.preys.values())
np.random.shuffle(preys)
for i in range(num):
prey = preys[i * 2]
candidate_agent = preys[i * 2 + 1]
child = Agent()
child.id = self.max_id
self.max_id += 1
child.predator = False
child.life = np.random.normal(500, scale=100)
child.health = 1
child.attack = 1
rate = np.random.rand()
if np.random.rand() < mutation_prob:
child.resilience = (
rate * prey.gene_resilience + (1 - rate) *
candidate_agent.gene_resilience) + np.random.normal()
else:
child.resilience = (
rate * prey.gene_resilience +
(1 - rate) * candidate_agent.gene_resilience)
rate = np.random.rand()
if np.random.rand() < mutation_prob:
speed = (rate * prey.gene_speed + (1 - rate) *
candidate_agent.gene_speed) + np.random.normal()
speed = np.clip(speed, self.min_speed, self.max_speed)
child.speed = int(speed)
else:
child.speed = int(
np.round(rate * prey.gene_speed +
(1 - rate) * candidate_agent.gene_speed))
child.gene_speed = child.speed
child.gene_attack = child.attack
child.gene_resilience = child.resilience
child.gene_speed = child.speed
prey.reward = child.gene_attack + child.gene_resilience
candidate_agent.reward = child.gene_attack + child.gene_resilience
new_embedding = np.random.normal(size=[self.agent_emb_dim])
self.agent_embeddings[child.id] = new_embedding
child.hunt_square = self.max_hunt_square
child.property = [self._gen_power(child.id), [1, 0, 0]]
x = ind[0][perm[i]]
y = ind[1][perm[i]]
index += 1
self.map[x][y] = child.id
self.large_map[
x:self.large_map.shape[0]:self.map.shape[0],
y:self.large_map.shape[1]:self.map.shape[1]] = child.id
child.pos = (x, y)
self.preys[child.id] = child
self.prey_num += 1