def nextMoveOne(self, state, pacmanAction):
'''
For Q-Learning use. Don't loop through all possible next states,
just calculate one next state.
ARGS:
state: current Game state, numbering from 0 to num_states-1
pacmanAction: action for pacman to take, numbering from 0 to 3 for the four directions
RETURN:
nextState: next state returned by environment after taking the action
reward: reward at next state
done: whether next state is an end state
'''
pacmanLocX, pacmanLocY, ghostLocX, ghostLocY = self.state2coord(state)
# Move pacman
pacmanLocX_next, pacmanLocY_next = self.move(pacmanLocX, pacmanLocY,
pacmanAction)
# Get next action for chasing ghost
t_x, t_y = ghost_move(pacmanLocX, pacmanLocY, ghostLocX, ghostLocY,
self.num_ghosts, self.grid, self.ghost_type)
if (not self.moveThrough(pacmanLocX_next, pacmanLocY_next, pacmanLocX,
pacmanLocY, t_x, t_y, ghostLocX, ghostLocY)):
nextState = self.coord2state(pacmanLocX_next, pacmanLocY_next, t_x,
t_y)
reward, done = self.calculate_reward(pacmanLocX_next,
pacmanLocY_next, t_x, t_y)
else:
nextState = self.coord2state(pacmanLocX_next, pacmanLocY_next,
ghostLocX, ghostLocY)
reward = self.loseReward
done = True
return nextState, reward, done
def nextMove(self, currState, action):
'''
Returns all possible next states given action, and the reward given next states.
ARGS:
currState: Current game state, ranges from 0 to numStates-1.
action: Action to perform.
RETURN:
nextStates: List of possible next states give current state and action.
Largly depends on potential ghost movements.
rewards: List of rewards for each potential next state.
'''
nextStates = []
# possible next states
rewards = []
# rewards for each next state
done = []
# whether game is done for each next state
probs = []
# transition probs for each next state
if 'Chase' in self.ghost_type:
defaultProb = 1 / 4
else:
defaultProb = 1 / math.pow(4, self.num_ghosts)
# Get currState coordinates
pacmanLocX, pacmanLocY, ghostLocX, ghostLocY = self.state2coord(
currState)
# Check whether current state w/in bounds
if (self.grid[pacmanLocY][pacmanLocX] == True):
return probs, nextStates, rewards, done
# Check whether current state is in done state: eaten by ghost or eaten pellet
_, currStateDone = self.calculate_reward(pacmanLocX, pacmanLocY,
ghostLocX, ghostLocY)
if (currStateDone):
return probs, nextStates, rewards, done
# Get pacman location after performing action
pacmanLocX_next, pacmanLocY_next = self.move(pacmanLocX, pacmanLocY,
action)
## Get all possible ghost states
# Get next location from chasing ghost
t_x, t_y = ghost_move(pacmanLocX, pacmanLocY, ghostLocX, ghostLocY,
self.num_ghosts, self.grid, ['Random', 'Chase'])
chaseLocX, chaseLocY = t_x[1], t_y[1]
if (chaseLocX > ghostLocX[1]):
chaseGhostAction = RIGHT
elif (chaseLocX < ghostLocX[1]):
chaseGhostAction = LEFT
elif (chaseLocY > ghostLocY[1]):
chaseGhostAction = UP
else:
chaseGhostAction = DOWN
for i in range(4): # need to be hardcoded, <numGhosts> number of loops
# Get ghost next location given action
ghostLocX_next, ghostLocY_next = self.evalGhostAction(
ghostLocX, ghostLocY, [i, chaseGhostAction])
# if pacman not moving through ghost
if (not self.moveThrough(pacmanLocX_next, pacmanLocY_next,
pacmanLocX, pacmanLocY, ghostLocX_next,
ghostLocY_next, ghostLocX, ghostLocY)):
state = self.coord2state(pacmanLocX_next, pacmanLocY_next,
ghostLocX_next, ghostLocY_next)
nextStates.append(state)
reward, doneStatus = self.calculate_reward(
pacmanLocX_next, pacmanLocY_next, ghostLocX_next,
ghostLocY_next)
rewards.append(reward)
done.append(doneStatus)
else: # if pacman moving through ghost, set pacman as new state and keep old ghost states so pacman and ghost overlaps and game ends
state = self.coord2state(pacmanLocX_next, pacmanLocY_next,
ghostLocX, ghostLocY)
nextStates.append(state)
rewards.append(self.loseReward)
done.append(True)
probs.append(defaultProb)
return probs, nextStates, rewards, done