def getActionAndUpdate(self, state, reward):
action = np.random.choice(self.actions, 1, p=hp.PRIOR)[0]
# If Mario is stuck, overwrite action with jump
if state is not None:
mpos = feat.marioPosition(state.getTiles())
if mpos is not None:
if feat.stuck(state.getTiles(), mpos):
self.stuck_duration += 1
# If stuck for too long, rescue him
if self.stuck_duration > hp.STUCK_DURATION:
print "MODEL: Mario is stuck. Forcing jump to rescue..."
# On ground, get started with jump
if feat.groundVertDistance(state.getTiles(),
mpos) == 0:
action = random.choice([0, 10])
# Jump!
else:
action = 10
self.jumps += 1
# Stop jumping and reset
if self.jumps > hp.MAX_JUMPS:
self.jumps = 0
self.stuck_duration = 0
return action
def getActionAndUpdate(self, state, reward):
state = state.getTiles()
mpos = ft.marioPosition(state)
action = None
# Get Mario unstuck
if self.stuck_duration > 30:
print "Stuck!"
if self.jumps > 20:
self.jumps = 0
self.stuck_duration = 0
self.jumps += 1
if ft.groundVertDistance(state, mpos) == 0:
action = random.choice([10, 0])
else:
action = 10
elif mpos is not None:
# Check if stuck
if not ft.canMoveRight(state, mpos):
self.stuck_duration += 1
# Check if enemy left
if ft.distRightEnemy(state, mpos) <= 0.2 and ft.distUpEnemy(
state, mpos) < 0.1 and ft.distDownEnemy(state, mpos) < 0.2:
print "Enemy ahead!"
action = random.choice([0, 11])
# Check if enemy right
elif 0.0625 < ft.distLeftEnemy(state, mpos) <= 0.1:
print("Enemy behind!", ft.distLeftEnemy(state, mpos))
action = random.choice([0, 11])
# Check if gap
elif ft.groundRightDistance(state, mpos) <= 0.3:
print "Gap ahead!"
if ft.groundRightDistance(state, mpos) <= 0.06:
print "Need to jump!"
action = 10
else:
action = 9
random_action = np.random.choice(self.actions, 1, p=hp.PRIOR)[0]
if not action:
return random_action
else:
return np.random.choice([action, random_action], 1, p=[0.99,
0.01])[0]
i = 1
# Begin training loop
while i <= hp.TRAINING_ITERATIONS:
print('-- Resetting agent')
agent.reset()
rewardFunction.reset()
print('-- START playing iteration %d / %d' %
(i + j, hp.TRAINING_ITERATIONS + j))
# Sample first action randomly
action = env.action_space.sample()
state = None
while (ft.marioPosition(state) is None):
state, reward, _, info = env.step(action)
state = util.State(state, info['distance'], None)
# Compute custom reward
reward = rewardFunction.getReward(reward, info)
dead = win = False
# Begin main action-perception loop
while not (info['iteration'] > i):
# Check if Mario is at the end of the level
if info['distance'] >= hp.LEVEL_WIN_DIST:
win = True