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
