def step(self, action):
action = action[0]
# Force FIRE action to start episodes in games with lives
if self._force_fire:
obs, _, _, _ = self.env.env.step(1)
self._force_fire = False
while self._current_no_op > 0:
obs, _, _, _ = self.env.env.step(0)
self._current_no_op -= 1
obs, reward, absorbing, info = self.env.step(action)
self._real_reset = absorbing
if info['lives'] != self._lives:
if self._episode_ends_at_life:
absorbing = True
self._lives = info['lives']
self._force_fire = self.env.unwrapped.get_action_meanings(
)[1] == 'FIRE'
self._state.append(preprocess_frame(obs, self._img_size))
return LazyFrames(list(self._state),
self._history_length), reward, absorbing, info
def reset(self, state=None):
self._state = preprocess_frame(self.env.reset(), self._img_size)
self._state = deque([deepcopy(
self._state) for _ in range(self._history_length)],
maxlen=self._history_length
)
return LazyFrames(list(self._state), self._history_length)
def step(self, action):
obs, reward, absorbing, info = self.env.step(action)
reward *= 1. # Int to float
if reward > 0:
reward = 1. # MiniGrid discounts rewards based on timesteps, but we need raw rewards
self._state.append(preprocess_frame(obs, self._img_size))
return LazyFrames(list(self._state),
self._history_length), reward, absorbing, info
def reset(self, state=None):
if self._real_reset:
self._state = preprocess_frame(self.env.reset(), self._img_size)
self._state = deque(
[deepcopy(self._state) for _ in range(self._history_length)],
maxlen=self._history_length)
self._lives = self._max_lives
self._force_fire = self.env.unwrapped.get_action_meanings(
)[1] == 'FIRE'
self._current_no_op = np.random.randint(self._max_no_op_actions + 1)
return LazyFrames(list(self._state), self._history_length)