def env_step(self, thisAction):
intAction = thisAction.intArray[0]
obs, reward = self.takeAction(intAction)
theObs = obs
returnRO = Reward_observation_terminal()
returnRO.r = reward
returnRO.o = theObs
returnRO.terminal = mdptetris.isgameover()
return returnRO
def env_step(self,thisAction):
intAction = thisAction.intArray[0]
obs, reward = self.takeAction(intAction)
theObs = obs
returnRO = Reward_observation_terminal()
returnRO.r = reward
returnRO.o = theObs
returnRO.terminal = mdptetris.isgameover()
return returnRO
def takeAction(self, intAction):
# intAction is interpreted as an index into the
# cross product between columns and rotations.
rotation, column = intAction % 4, int(intAction/4)+1
# Next, the game restricts the rotations and columns based
# on the current piece. So, we map the selected rotation and column appropriately
rotation %= mdptetris.num_rotate_actions()
column = min(column, mdptetris.num_column_actions(rotation))
# Take the action
lines_cleared = mdptetris.drop_piece(rotation, column)
#if lines_cleared > 0:
# print "Cleared!"
#print "Action", rotation, column
obs = self.getObservation()
reward = self.computeReward(lines_cleared) if not mdptetris.isgameover() else -1.0
return obs, reward
def takeAction(self, intAction):
# intAction is interpreted as an index into the
# cross product between columns and rotations.
rotation, column = intAction % 4, int(intAction / 4) + 1
# Next, the game restricts the rotations and columns based
# on the current piece. So, we map the selected rotation and column appropriately
rotation %= mdptetris.num_rotate_actions()
column = min(column, mdptetris.num_column_actions(rotation))
# Take the action
lines_cleared = mdptetris.drop_piece(rotation, column)
#if lines_cleared > 0:
# print "Cleared!"
#print "Action", rotation, column
obs = self.getObservation()
reward = self.computeReward(
lines_cleared) if not mdptetris.isgameover() else -1.0
return obs, reward
