class MSBSimulatedEnemyAgent:
def __init__(self, index, distancer, uniform = False):
self.index = index
self.distancer = distancer
self.weights = {
"default" : 1
}
if uniform:
self.agent = self
return
#currently, this uses BaselineAgents' agents.
#To use the features/weights in this class, set self.agent = self
try:
from BaselineAgents.baselineAgents import OffensiveReflexAgent, DefensiveReflexAgent
self.agent = OffensiveReflexAgent(index) if index%2==0 else DefensiveReflexAgent(index)
self.agent.distancer = distancer
except: #if BaselineAgents isn't accessible, fallback gracefully
self.agent = self
def getFeatures(self, state, action):
return {"default":1}
def evaluate(self, state, action):
#fall through to another agent if we have one
if self.agent is None:
return 0
elif self.agent != self:
return max(0, self.agent.evaluate(state, action))
features = self.getFeatures(state, action)
amts = [features[i]*self.weights[i] if i in self.weights else 0 for i in features]
return max(sum(amts), 0)
def getDistribution(self, gameState):
#get the utilities from the agent and find the maximum
utilities = {action: self.evaluate(gameState, action) for action in gameState.getLegalActions(self.index)}
maxUtility = max(utilities.values())
#any action that maximizes utility gets equal probability, all else get 0
tbr = util.Counter()
for action in utilities:
if utilities[action] < maxUtility:
continue
tbr[action] = 1
tbr.normalize()
return tbr
def __init__(self, index, distancer, uniform = False):
self.index = index
self.distancer = distancer
self.weights = {
"default" : 1
}
if uniform:
self.agent = self
return
#currently, this uses BaselineAgents' agents.
#To use the features/weights in this class, set self.agent = self
try:
from BaselineAgents.baselineAgents import OffensiveReflexAgent, DefensiveReflexAgent
self.agent = OffensiveReflexAgent(index) if index%2==0 else DefensiveReflexAgent(index)
self.agent.distancer = distancer
except: #if BaselineAgents isn't accessible, fallback gracefully
self.agent = self