def __init__(self,
width=11,
height=11,
preyLocation=(5, 5),
predatorLocation=(0, 0),
numberOfPredators=1):
assert numberOfPredators > 0
assert numberOfPredators < 5
assert type(numberOfPredators) == int
self.width = width
self.height = height
self.numberOfPredators = numberOfPredators
self.TeamPrey = TeamPrey(self, preyLocation)
self.TeamPredator = TeamPredator(self, predatorLocation)
def __init__( self,
width=11,
height=11,
preyLocation=(5,5),
predatorLocation=(0,0),
numberOfPredators=1 ):
assert numberOfPredators > 0
assert numberOfPredators < 5
assert type(numberOfPredators) == int
self.width = width
self.height = height
self.numberOfPredators = numberOfPredators
self.TeamPrey = TeamPrey( self, preyLocation )
self.TeamPredator = TeamPredator(self, predatorLocation)
class Environment:
'''
Creates an instance of the environment. Default an eleven by eleven grid
is used. The default position for the prey is (5,5).
'''
def __init__( self,
width=11,
height=11,
preyLocation=(5,5),
predatorLocation=(0,0),
numberOfPredators=1 ):
assert numberOfPredators > 0
assert numberOfPredators < 5
assert type(numberOfPredators) == int
self.width = width
self.height = height
self.numberOfPredators = numberOfPredators
self.TeamPrey = TeamPrey( self, preyLocation )
self.TeamPredator = TeamPredator(self, predatorLocation)
def minimaxQLearning( self, episodes = 1000 ):
done = False
self.resetAgents()
s = self.gameState()
episode = 0
while not done:
# In state s take action a and opponent action o to get reward r and
# state s_prime
# Find the optimal action, epsilon greedy
a_teamPred = self.TeamPredator.getJointActionEpsilonGreedy( s )
a_teamPrey = self.TeamPrey.getActionEpsilonGreedy( s )
# Each team performs the found action
self.TeamPredator.performAction( a_teamPred )
self.TeamPrey.performAction( a_teamPrey )
s_prime = self.gameState()
reward, game_over = self.reward(s_prime)
# update Q : State , own action, opponent action
self.TeamPredator.updateQ(s, a_teamPred, a_teamPrey, s_prime, reward)
self.TeamPrey.updateQ(s, a_teamPrey, a_teamPred, s_prime, reward)
s = s_prime
if reward != 0:
if episode < episodes:
episode += 1
self.resetAgents()
else:
done = True
def reward( self, s ):
'''
r, game_over <- reward(s)
Returns the reward for a given state containing location of
the predators and prey. Return is the reward of the predator(s), as
this is a zero-sum-game, the reward of the predator is simply the
negated reward of the prey. Boolean game_over indicates if the state
is absorbing.
'''
# Prioritize confusion of predators
if len(s) != len(set(s)):
return -10, True
elif (0,0) in s:
return 10, True
else:
return 0, False
def deriveState( self, locations ):
'''
state <- deriveState( locations )
Derive the state based on the locations of the prey and the predators.
'''
prey_x, prey_y = locations[0]
predator_x, predator_y = locations[1]
x = ( ( 5 + prey_x - predator_x ) % ( self.width ) ) - 5
y = ( ( 5 + prey_y - predator_y ) % ( self.height ) ) - 5
return (x,y)
def gameState( self ):
'''
state <- deriveState( gameState )
Derives the gamestate based on agents location
'''
prey_x, prey_y = self.TeamPrey.Prey.location
predator_x, predator_y = self.TeamPredator.Predator.location
x = ( ( 5 + prey_x - predator_x ) % ( self.width ) ) - 5
y = ( ( 5 + prey_y - predator_y ) % ( self.height ) ) - 5
return (x,y)
def resetAgents(self):
'''
Reset the position of the prey and predator in this environment.
'''
self.TeamPrey.Prey.location = (5,5)
self.TeamPredator.Predator.location = (random.randint(-5,5), random.randint(-5,5))
def simulateEnvironment(self):
''''
simulateEnvironment(reset=False)
Simulate the environment for one step. Location of each agent is
updated. Returns a list of agent locations.
'''
s = self.gameState()
for Agent in self.Agents:
# Get an action based on Q
a = Agent.getActionEpsilonGreedy(s)
# Update location
Agent.performAction(a)
class Environment:
'''
Creates an instance of the environment. Default an eleven by eleven grid
is used. The default position for the prey is (5,5).
'''
def __init__(self,
width=11,
height=11,
preyLocation=(5, 5),
predatorLocation=(0, 0),
numberOfPredators=1):
assert numberOfPredators > 0
assert numberOfPredators < 5
assert type(numberOfPredators) == int
self.width = width
self.height = height
self.numberOfPredators = numberOfPredators
self.TeamPrey = TeamPrey(self, preyLocation)
self.TeamPredator = TeamPredator(self, predatorLocation)
def minimaxQLearning(self, episodes=1000):
done = False
self.resetAgents()
s = self.gameState()
episode = 0
while not done:
# In state s take action a and opponent action o to get reward r and
# state s_prime
# Find the optimal action, epsilon greedy
a_teamPred = self.TeamPredator.getJointActionEpsilonGreedy(s)
a_teamPrey = self.TeamPrey.getActionEpsilonGreedy(s)
# Each team performs the found action
self.TeamPredator.performAction(a_teamPred)
self.TeamPrey.performAction(a_teamPrey)
s_prime = self.gameState()
reward, game_over = self.reward(s_prime)
# update Q : State , own action, opponent action
self.TeamPredator.updateQ(s, a_teamPred, a_teamPrey, s_prime,
reward)
self.TeamPrey.updateQ(s, a_teamPrey, a_teamPred, s_prime, reward)
s = s_prime
if reward != 0:
if episode < episodes:
episode += 1
self.resetAgents()
else:
done = True
def reward(self, s):
'''
r, game_over <- reward(s)
Returns the reward for a given state containing location of
the predators and prey. Return is the reward of the predator(s), as
this is a zero-sum-game, the reward of the predator is simply the
negated reward of the prey. Boolean game_over indicates if the state
is absorbing.
'''
# Prioritize confusion of predators
if len(s) != len(set(s)):
return -10, True
elif (0, 0) in s:
return 10, True
else:
return 0, False
def deriveState(self, locations):
'''
state <- deriveState( locations )
Derive the state based on the locations of the prey and the predators.
'''
prey_x, prey_y = locations[0]
predator_x, predator_y = locations[1]
x = ((5 + prey_x - predator_x) % (self.width)) - 5
y = ((5 + prey_y - predator_y) % (self.height)) - 5
return (x, y)
def gameState(self):
'''
state <- deriveState( gameState )
Derives the gamestate based on agents location
'''
prey_x, prey_y = self.TeamPrey.Prey.location
predator_x, predator_y = self.TeamPredator.Predator.location
x = ((5 + prey_x - predator_x) % (self.width)) - 5
y = ((5 + prey_y - predator_y) % (self.height)) - 5
return (x, y)
def resetAgents(self):
'''
Reset the position of the prey and predator in this environment.
'''
self.TeamPrey.Prey.location = (5, 5)
self.TeamPredator.Predator.location = (random.randint(-5, 5),
random.randint(-5, 5))
def simulateEnvironment(self):
''''
simulateEnvironment(reset=False)
Simulate the environment for one step. Location of each agent is
updated. Returns a list of agent locations.
'''
s = self.gameState()
for Agent in self.Agents:
# Get an action based on Q
a = Agent.getActionEpsilonGreedy(s)
# Update location
Agent.performAction(a)
