def getAction(self, game_state: GameState) -> str:
"""
Returns the minimax action from the current gameState using
self.depth and self.evaluationFunction.
Here are some method calls that might be useful when implementing
minimax.
gameState.getLegalActions(agentIndex): Returns a list of legal
actions for an agent agentIndex=0 means Pacman, ghosts are >= 1
gameState.generateSuccessor(agentIndex, action): Returns the
successor game state after an agent takes an action
gameState.getNumAgents(): Returns the total number of agents in the
game """
legal_actions = game_state.getLegalActions(agentIndex=0)
best_action_index = max(range(len(legal_actions)),
key=lambda action_num: self.min_value(
state=game_state.generateSuccessor(
agentIndex=0,
action=legal_actions[action_num],
),
depth=self.depth,
ghost_num=1,
))
return legal_actions[best_action_index]
def min_value(self, state: GameState, depth: int, ghost_num: int) -> int:
# Game over or search depth has been reached
if state.isLose() or state.isWin() or depth <= 0:
return self.evaluationFunction(state)
# Sanity check: valid ghost number?
assert 1 <= ghost_num < state.getNumAgents()
legal_actions = state.getLegalActions(ghost_num)
successors = [
state.generateSuccessor(ghost_num, ghost_action)
for ghost_action in legal_actions
]
# If this is the last ghost, next optimizer should be from pacman's
# perspective
next_optimizer = self.max_value \
if ghost_num == state.getNumAgents() - 1 \
else self.min_value
# If this is the last ghost, decrement depth
next_depth = depth - 1 \
if ghost_num == state.getNumAgents() - 1 \
else depth
utilities = [
next_optimizer(state, next_depth, ghost_num + 1)
for state in successors
]
return min(utilities)
def searchTree(state: GameState, depth: int, agent: int):
actions = state.getLegalActions(agent)
nextAgent = (agent + 1) % state.getNumAgents()
if state.isLose() or state.isWin() or len(actions) == 0:
return [state.getScore(), None]
elif depth == 0:
return [self.evaluationFunction(state), None]
elif agent == 0:
successors = [
searchTree(state.generateSuccessor(agent, action), depth,
nextAgent)[0] for action in actions
]
maximum = max(successors)
maxIndex = successors.index(maximum)
return [maximum, actions[maxIndex]]
else:
nextDepth = depth
if nextAgent == 0:
nextDepth -= 1
successors = [
searchTree(state.generateSuccessor(agent, action),
nextDepth, nextAgent)[0] for action in actions
]
expected = sum(successors) * 1.0 / len(successors)
return [expected, None]
def _alphabeta(self, gameState: GameState, idx: int, ab: List[float]) -> Tuple[float, str]:
n = gameState.getNumAgents()
if idx / n >= self.depth or gameState.isWin() or gameState.isLose():
return (self.evaluationFunction(gameState), None)
agent = idx % n
legalActions = gameState.getLegalActions(agent)
pacman = (agent == 0)
idx0 = int(pacman)
idx1 = int(not pacman)
mod = 1 if pacman else -1
best_score = -float('inf') * mod
best_action = None
for legalAction in legalActions:
s = gameState.generateSuccessor(agent, legalAction)
score = self._alphabeta(s, idx + 1, [*ab])[0]
if score * mod > best_score * mod:
best_score, best_action = score, legalAction
if best_score * mod > ab[idx0] * mod:
break
ab[idx1] = max(ab[idx1] * mod, best_score * mod) * mod
return (best_score, best_action)
def getAction(self, gameState: GameState):
"""
You do not need to change this method, but you're welcome to.
getAction chooses among the best options according to the evaluation function.
Just like in the previous project, getAction takes a GameState and returns
some Directions.X for some X in the set {NORTH, SOUTH, WEST, EAST, STOP}
"""
# Collect legal moves and successor states
legalMoves = gameState.getLegalActions()
# print(legalMoves)
# Choose one of the best actions
scores = [self.evaluationFunction(
gameState, action) for action in legalMoves]
# print(scores)
bestScore = max(scores)
# print(bestScore)
bestIndices = [index for index in range(
len(scores)) if scores[index] == bestScore]
# print(bestIndices)
# Pick randomly among the best
chosenIndex = random.choice(bestIndices)
# print(chosenIndex)
"Add more of your code here if you want to"
return legalMoves[chosenIndex]
def getAction(self, game_state: GameState):
"""
Returns the minimax action using self.depth and self.evaluationFunction
"""
"*** YOUR CODE HERE ***"
# Generate candidate actions
legal_actions = game_state.getLegalActions(self.pacman_index)
# if Directions.STOP in legal_actions:
# legal_actions.remove(Directions.STOP)
alpha = -math.inf
beta = math.inf
scores = []
for action in legal_actions:
successor = game_state.getNextState(self.pacman_index, action)
# since we're expanding the root node, we need to call min_value since the next node is a min node
value = self.min_value(successor, depth=0, ghost_index=0, alpha=alpha, beta=beta)
scores.append(value)
# can't prune on the root node
alpha = max(alpha, value)
best_score = max(scores)
best_indices = [index for index in range(len(scores)) if scores[index] == best_score]
chosen_index = random.choice(best_indices) # Pick randomly among the best
return legal_actions[chosen_index]
def rb_directional_expectimax(self, cur_state: GameState, turn: int,
agent: int, depth_limit: int, depth: int,
ghost_num: int):
if turn == agent:
depth += 1
if depth >= depth_limit or cur_state.isWin() or cur_state.isLose():
return self.evaluationFunction(cur_state)
if turn == agent: # if Pacman's turn
cur_max = np.NINF
for action in cur_state.getLegalPacmanActions(
): # iterating over children gameStates
child_state = cur_state.generateSuccessor(turn, action)
cur_max = max(
cur_max,
self.rb_directional_expectimax(
child_state, (turn + 1) % (ghost_num + 1), agent,
depth_limit, depth, ghost_num))
return cur_max
else: # if ghost turn
assert turn > agent
ghost_legal_moves = cur_state.getLegalActions(turn)
ghost = DirectionalGhost(turn)
# assert len(ghost_legal_moves) is not 0
expectancy = 0
for action in ghost_legal_moves:
child_state = cur_state.generateSuccessor(turn, action)
dist = ghost.getDistribution(cur_state)
# print(dist)
expectancy += (dist[action]) * (self.rb_directional_expectimax(
child_state, (turn + 1) %
(ghost_num + 1), agent, depth_limit, depth, ghost_num))
if math.isnan(expectancy):
expectancy = 0
return expectancy
def min_value(self, game_state: GameState, depth=0, ghost_index=0, alpha=-math.inf, beta=math.inf):
# next_ghost_to_move
ghost_index += 1
if self.is_a_new_level_of_search(game_state, ghost_index):
depth = depth + 1
if game_state.isWin() or game_state.isLose():
return self.evaluationFunction(game_state)
value = math.inf
legal_actions = game_state.getLegalActions(ghost_index)
for action in legal_actions:
successor = game_state.getNextState(ghost_index, action)
if self.is_a_new_level_of_search(game_state, ghost_index):
# let's move on with pacman since this is the last agent (new max node)
value = min(value, self.max_value(successor, depth=depth, alpha=alpha, beta=beta))
else:
# next on the tree is another minimizer, lets continue with another ghost
value = min(value,
self.min_value(successor, depth=depth, ghost_index=ghost_index, alpha=alpha, beta=beta))
if value < alpha:
return value
beta = min(beta, value)
return value
def max_value(
self,
state: GameState,
depth: int,
alpha: int,
beta: int,
actor: Optional[int] = None,
) -> int:
# Sanity check: have all the ghosts been evaluated the last round?
if actor is not None:
assert actor == state.getNumAgents()
# Game over or search depth has been reached
if state.isLose() or state.isWin() or depth <= 0:
return self.evaluationFunction(state)
legal_actions = state.getLegalActions(agentIndex=0)
utility = -inf
for action in legal_actions:
successor = state.generateSuccessor(agentIndex=0, action=action)
utility = max(
utility,
self.min_value(successor, depth, alpha, beta, ghost_num=1),
)
if utility > beta:
return utility
alpha = max(alpha, utility)
return utility
def exp_value(self, game_state: GameState, depth=0, ghost_index=0):
# next_ghost_to_move
ghost_index += 1
if self.is_a_new_level_of_search(game_state, ghost_index):
depth = depth + 1
if game_state.isWin() or game_state.isLose():
return self.evaluationFunction(game_state)
value = 0
legal_actions = game_state.getLegalActions(ghost_index)
for action in legal_actions:
successor = game_state.getNextState(ghost_index, action)
probability = 1 / len(legal_actions)
if self.is_a_new_level_of_search(game_state, ghost_index):
# let's move on with pacman since this is the last agent (new max node)
value += probability * self.max_value(successor, depth=depth)
else:
# next on the tree is another minimizer, lets continue with another ghost
value += probability * self.exp_value(successor, depth=depth, ghost_index=ghost_index)
return value
def getAction(self, gameState: GameState) -> str:
"""
Returns the minimax action using self.depth and self.evaluationFunction
"""
# BEGIN_YOUR_CODE (our solution is 36 lines of code, but don't worry if you deviate from this)
def getVal(s,
d,
agentIndex,
alpha=float('-inf'),
beta=float('inf'),
evalFn=self.evaluationFunction):
nextAgentIndex = 0 if agentIndex == s.getNumAgents(
) - 1 else agentIndex + 1
actions = s.getLegalActions(agentIndex)
if len(actions) == 0:
return s.getScore()
elif d == 0:
if agentIndex != 0:
raise Exception(
f"Unexpected agentIndex {agentIndex} != {0}")
return evalFn(s)
elif agentIndex == 0:
maxVal = float('-inf')
# actions.sort(key=lambda a: evalFn(s.generateSuccessor(agentIndex, a)), reverse=True)
for a in actions:
maxVal = max(
maxVal,
getVal(s.generateSuccessor(agentIndex, a), d,
nextAgentIndex, alpha, beta))
alpha = max(alpha, maxVal)
if alpha >= beta:
break
return maxVal
else:
nextD = d - (1 if agentIndex == s.getNumAgents() - 1 else 0)
minVal = float('inf')
# actions.sort(key=lambda a: evalFn(s.generateSuccessor(agentIndex, a)), reverse=False)
for a in actions:
minVal = min(
minVal,
getVal(s.generateSuccessor(agentIndex, a), nextD,
nextAgentIndex, alpha, beta))
beta = min(beta, minVal)
if alpha >= beta:
break
return minVal
targetVal = getVal(gameState, self.depth, 0)
# print(f"AlphaBetaAgent value of state = {targetVal}")
legalActions = gameState.getLegalActions(0)
actions = [
a for a in legalActions if getVal(
gameState.generateSuccessor(0, a), self.depth, 1) == targetVal
]
return random.choice(actions)
def getAction(self, gameState: GameState):
"""
getAction chooses among the best options according to the evaluation function.
getAction takes a GameState and returns some Directions.X for some X in the set {North, South, West, East}
------------------------------------------------------------------------------
Description of GameState and helper functions:
A GameState specifies the full game state, including the food, capsules,
agent configurations and score changes. In this function, the |gameState| argument
is an object of GameState class. Following are a few of the helper methods that you
can use to query a GameState object to gather information about the present state
of Pac-Man, the ghosts and the maze.
gameState.getLegalActions(agentIndex):
Returns the legal actions for the agent specified. Returns Pac-Man's legal moves by default.
gameState.generateSuccessor(agentIndex, action):
Returns the successor state after the specified agent takes the action.
Pac-Man is always agent 0.
gameState.getPacmanState():
Returns an AgentState object for pacman (in game.py)
state.configuration.pos gives the current position
state.direction gives the travel vector
gameState.getGhostStates():
Returns list of AgentState objects for the ghosts
gameState.getNumAgents():
Returns the total number of agents in the game
gameState.getScore():
Returns the score corresponding to the current state of the game
The GameState class is defined in pacman.py and you might want to look into that for
other helper methods, though you don't need to.
"""
# Collect legal moves and successor states
legalMoves = gameState.getLegalActions()
# Choose one of the best actions
scores = [
self.evaluationFunction(gameState, action) for action in legalMoves
]
bestScore = max(scores)
bestIndices = [
index for index in range(len(scores)) if scores[index] == bestScore
]
chosenIndex = random.choice(
bestIndices) # Pick randomly among the best
return legalMoves[chosenIndex]
def min_value(
self,
state: GameState,
depth: int,
alpha: int,
beta: int,
ghost_num: int,
) -> int:
# Game over or search depth has been reached
if state.isLose() or state.isWin() or depth <= 0:
return self.evaluationFunction(state)
# Sanity check: valid ghost number?
assert 1 <= ghost_num < state.getNumAgents()
legal_actions = state.getLegalActions(ghost_num)
# If this is the last ghost, next optimizer should be from pacman's
# perspective
next_optimizer = self.max_value \
if ghost_num == state.getNumAgents() - 1 \
else self.min_value
# If this is the last ghost, decrement depth
next_depth = depth - 1 if ghost_num == state.getNumAgents(
) - 1 else depth
utility = inf
for action in legal_actions:
successor = state.generateSuccessor(
agentIndex=ghost_num,
action=action,
)
utility = min(
utility,
next_optimizer(
successor,
next_depth,
alpha,
beta,
ghost_num + 1,
),
)
if utility < alpha:
return utility
beta = min(beta, utility)
return utility
def max_value(self, game_state: GameState, depth):
if self.is_terminal_state(game_state, depth):
return self.evaluationFunction(game_state)
value = -math.inf
legal_actions = game_state.getLegalActions(self.pacman_index)
for action in legal_actions:
successor = game_state.getNextState(self.pacman_index, action)
value = max(value, self.exp_value(successor, depth=depth, ghost_index=0))
return value
def getAction(self, gameState: GameState):
"""
Returns the minimax action from the current gameState using self.depth
and self.evaluationFunction.
"""
_max = float("-inf")
action = None
for move in gameState.getLegalActions(0):
util = minimax(self.evaluationFunction, 1, 0,
gameState.generateSuccessor(0, move), self.depth)
if util > _max or _max == float("-inf"):
_max = util
action = move
return action
def expimax(evalFunc: classmethod, agent: int, depth: int, gameState: GameState, maxDepth: int) -> float:
if gameState.isLose() or gameState.isWin() or depth == maxDepth:
return evalFunc(gameState)
if agent == 0:
return max(expimax(evalFunc, 1, depth, gameState.generateSuccessor(agent, action), maxDepth) for action in gameState.getLegalActions(agent))
else:
nextAgent = agent + 1
if gameState.getNumAgents() == nextAgent:
nextAgent = 0
if nextAgent == 0:
depth += 1
val = 0
for action in gameState.getLegalActions(agent):
val += expimax(evalFunc, nextAgent, depth,
gameState.generateSuccessor(agent, action), maxDepth)
return val
def max_value(self, game_state: GameState, depth, alpha=-math.inf, beta=math.inf):
if self.is_terminal_state(game_state, depth):
return self.evaluationFunction(game_state)
value = -math.inf
legal_actions = game_state.getLegalActions(self.pacman_index)
for action in legal_actions:
successor = game_state.getNextState(self.pacman_index, action)
value = max(value, self.min_value(successor, depth=depth, ghost_index=0, alpha=alpha, beta=beta))
if value > beta:
return value
alpha = max(alpha, value)
return value
def _minimax(self, gameState: GameState, idx: int) -> Tuple[float, str]:
n = gameState.getNumAgents()
if idx / n >= self.depth or gameState.isWin() or gameState.isLose():
return (self.evaluationFunction(gameState), None)
agent = idx % n
legalActions = gameState.getLegalActions(agent)
mod = 1 if agent == 0 else -1
best_score = -float('inf') * mod
best_action = None
for legalAction in legalActions:
s = gameState.generateSuccessor(agent, legalAction)
score = self._minimax(s, idx + 1)[0]
if score * mod > best_score * mod:
best_score, best_action = score, legalAction
return (best_score, best_action)
def getAction(self, game_state: GameState) -> str:
"""
Returns the minimax action from the current gameState using
self.depth and self.evaluationFunction.
Here are some method calls that might be useful when implementing
minimax.
gameState.getLegalActions(agentIndex): Returns a list of legal
actions for an agent agentIndex=0 means Pacman, ghosts are >= 1
gameState.generateSuccessor(agentIndex, action): Returns the
successor game state after an agent takes an action
gameState.getNumAgents(): Returns the total number of agents in the
game
"""
legal_actions = game_state.getLegalActions(agentIndex=0)
alpha, beta = -inf, inf
utility = -inf
for action_num in range(len(legal_actions)):
successor = game_state.generateSuccessor(
agentIndex=0,
action=legal_actions[action_num],
)
utility = max(
utility,
self.min_value(
successor,
depth=self.depth,
alpha=alpha,
beta=beta,
ghost_num=1,
),
)
if utility > alpha:
best_action_index = action_num
alpha = utility
return legal_actions[best_action_index]
def getAction(self, game_state: GameState):
"""
Returns the minimax action from the current gameState using self.depth
and self.evaluationFunction.
Here are some method calls that might be useful when implementing minimax.
gameState.getLegalActions(agentIndex):
Returns a list of legal actions for an agent
agentIndex=0 means Pacman, ghosts are >= 1
gameState.getNextState(agentIndex, action):
Returns the child game state after an agent takes an action
gameState.getNumAgents():
Returns the total number of agents in the game
gameState.isWin():
Returns whether or not the game state is a winning state
gameState.isLose():
Returns whether or not the game state is a losing state
"""
"*** YOUR CODE HERE ***"
# Generate candidate actions
legal_actions = game_state.getLegalActions(self.pacman_index)
# if Directions.STOP in legal_actions:
# legal_actions.remove(Directions.STOP)
# since we're expanding the root node, we need to call min_value since the next node is a min node
scores = [self.min_value(game_state.getNextState(self.pacman_index, action), depth=0, ghost_index=0) for action
in legal_actions]
best_score = max(scores)
best_indices = [index for index in range(len(scores)) if scores[index] == best_score]
chosen_index = random.choice(best_indices) # Pick randomly among the best
# input('next')
return legal_actions[chosen_index]
def getAction(self, gameState: GameState) -> str:
"""
Returns the expectimax action using self.depth and self.evaluationFunction
All ghosts should be modeled as choosing uniformly at random from their
legal moves.
"""
# BEGIN_YOUR_CODE (our solution is 20 lines of code, but don't worry if you deviate from this)
def getVal(s, d, agentIndex, evalFn=self.evaluationFunction):
nextAgentIndex = 0 if agentIndex == s.getNumAgents(
) - 1 else agentIndex + 1
actions = s.getLegalActions(agentIndex)
if len(actions) == 0:
return s.getScore()
elif d == 0:
if agentIndex != 0:
raise Exception(
f"Unexpected agentIndex {agentIndex} != {0}")
return evalFn(s)
elif agentIndex == 0:
return max(
getVal(s.generateSuccessor(agentIndex, a), d,
nextAgentIndex) for a in actions)
else:
nextD = d - (1 if agentIndex == s.getNumAgents() - 1 else 0)
return sum((1 / len(actions)) * getVal(
s.generateSuccessor(agentIndex, a), nextD, nextAgentIndex)
for a in actions)
targetVal = getVal(gameState, self.depth, 0)
# print(f"MinimaxAgent value of state = {targetVal}")
legalActions = gameState.getLegalActions(0)
actions = [
a for a in legalActions if getVal(
gameState.generateSuccessor(0, a), self.depth, 1) == targetVal
]
return random.choice(actions)
def max_value(self,
state: GameState,
depth: int,
actor: Optional[int] = None) -> int:
# Sanity check: have all the ghosts been evaluated the last round?
if actor is not None:
assert actor == state.getNumAgents()
# Game over or search depth has been reached
if state.isLose() or state.isWin() or depth <= 0:
return self.evaluationFunction(state)
legal_actions = state.getLegalActions(agentIndex=0)
successors = [
state.generateSuccessor(agentIndex=0, action=action)
for action in legal_actions
]
utilities = [
self.min_value(state, depth, ghost_num=1) for state in successors
]
return max(utilities)
def _expectimax(self, gameState: GameState, idx: int) -> Tuple[float, str]:
n = gameState.getNumAgents()
if idx / n >= self.depth or gameState.isWin() or gameState.isLose():
return (self.evaluationFunction(gameState), None)
agent = idx % n
legalActions = gameState.getLegalActions(agent)
n_actions = len(legalActions)
ret_score = -float('inf') if agent == 0 else 0
ret_action = None
for legalAction in legalActions:
s = gameState.generateSuccessor(agent, legalAction)
score = self._expectimax(s, idx + 1)[0]
if agent != 0:
ret_score += score / n_actions
elif score > ret_score:
ret_score, ret_action = score, legalAction
return (ret_score, ret_action)
def getAction(self, game_state: GameState):
"""
Returns the expectimax action using self.depth and self.evaluationFunction
All ghosts should be modeled as choosing uniformly at random from their
legal moves.
"""
"*** YOUR CODE HERE ***"
# Generate candidate actions
legal_actions = game_state.getLegalActions(self.pacman_index)
# if Directions.STOP in legal_actions:
# legal_actions.remove(Directions.STOP)
# since we're expanding the root node, we need to call min_value since the next node is a min node
scores = [self.exp_value(game_state.getNextState(self.pacman_index, action), depth=0, ghost_index=0) for action
in legal_actions]
best_score = max(scores)
best_indices = [index for index in range(len(scores)) if scores[index] == best_score]
chosen_index = random.choice(best_indices) # Pick randomly among the best
# input('next')
return legal_actions[chosen_index]
def searchTree(state: GameState, depth: int, agent: int, a, b):
actions = state.getLegalActions(agent)
nextAgent = (agent + 1) % state.getNumAgents()
if state.isLose() or state.isWin() or len(actions) == 0:
return [state.getScore(), None]
elif depth == 0:
return [self.evaluationFunction(state), None]
elif agent == 0:
value = float('-inf')
successors = []
for action in actions:
curr = searchTree(state.generateSuccessor(agent, action),
depth, nextAgent, a, b)[0]
successors.append(curr)
value = max(value, curr)
a = max(a, value)
if a >= b:
break
maxIndex = successors.index(value)
return [value, actions[maxIndex]]
else:
nextDepth = depth
if nextAgent == 0:
nextDepth -= 1
value = float('inf')
successors = []
for action in actions:
curr = searchTree(state.generateSuccessor(agent, action),
nextDepth, nextAgent, a, b)[0]
successors.append(curr)
value = min(value, curr)
b = min(b, value)
if a >= b:
break
minIndex = successors.index(value)
return [value, actions[minIndex]]
def getAction(self, gameState: GameState) -> str:
"""
Returns the minimax action from the current gameState using self.depth
and self.evaluationFunction. Terminal states can be found by one of the following:
pacman won, pacman lost or there are no legal moves.
Here are some method calls that might be useful when implementing minimax.
gameState.getLegalActions(agentIndex):
Returns a list of legal actions for an agent
agentIndex=0 means Pacman, ghosts are >= 1
gameState.generateSuccessor(agentIndex, action):
Returns the successor game state after an agent takes an action
gameState.getNumAgents():
Returns the total number of agents in the game
gameState.getScore():
Returns the score corresponding to the current state of the game
gameState.isWin():
Returns True if it's a winning state
gameState.isLose():
Returns True if it's a losing state
self.depth:
The depth to which search should continue
"""
# BEGIN_YOUR_CODE (our solution is 20 lines of code, but don't worry if you deviate from this)
def getVal(s, d, agentIndex, evalFn=self.evaluationFunction):
nextAgentIndex = 0 if agentIndex == s.getNumAgents(
) - 1 else agentIndex + 1
actions = s.getLegalActions(agentIndex)
if len(actions) == 0:
return s.getScore()
elif d == 0:
if agentIndex != 0:
raise Exception(
f"Unexpected agentIndex {agentIndex} != {0}")
return evalFn(s)
elif agentIndex == 0:
return max(
getVal(s.generateSuccessor(agentIndex, a), d,
nextAgentIndex) for a in actions)
else:
nextD = d - (1 if agentIndex == s.getNumAgents() - 1 else 0)
return min(
getVal(s.generateSuccessor(agentIndex, a), nextD,
nextAgentIndex) for a in actions)
targetVal = getVal(gameState, self.depth, 0)
# print(f"MinimaxAgent value of state = {targetVal}")
legalActions = gameState.getLegalActions(0)
actions = [
a for a in legalActions if getVal(
gameState.generateSuccessor(0, a), self.depth, 1) == targetVal
]
return random.choice(actions)
