def true_values_for_sample(self, states, actions, assume_optimal_policy: bool):
string_actions = []
for action in actions:
string_actions.append(self.features_to_action(action))
return GridworldBase.true_values_for_sample(
self, states, string_actions, assume_optimal_policy
)
def true_values_for_sample(self, states, actions, assume_optimal_policy: bool):
string_actions = []
for action in actions:
string_actions.append(self.features_to_action(action))
return GridworldBase.true_values_for_sample(
self, states, string_actions, assume_optimal_policy
)
def transition_probabilities(self, state, action) -> np.ndarray:
if action == "C":
next_state = self._cheat_step(state)
probabilities = np.zeros((self.width * self.height,))
probabilities[next_state] = 1
return probabilities
else:
return GridworldBase.transition_probabilities(self, state, action)
def true_values_for_sample(self, states, actions,
assume_optimal_policy: bool):
string_actions = []
for action in actions:
string_actions.append(self.ACTIONS[int(list(action.keys())[0]) -
self.num_states])
return GridworldBase.true_values_for_sample(self, states,
string_actions,
assume_optimal_policy)
def possible_next_actions(self, state, ignore_terminal=False) -> List[str]:
if ignore_terminal is False and self.is_terminal(state):
return []
possible_actions = GridworldBase.possible_next_actions(
self, state, ignore_terminal)
if ignore_terminal is False:
# Also ignore cheat actions when ignoring terminal
possible_actions.append('C')
return possible_actions
def step(self,
action: str,
with_possible=True) -> Tuple[int, float, bool, List[str]]:
if action == 'C':
self._state: int = self._cheat_step(self._state)
reward = self.reward(self._state)
possible_next_action = self.possible_next_actions(self._state)
return self._state, reward, self.is_terminal(
self._state), possible_next_action
else:
return GridworldBase.step(self, action)
def true_rewards_for_sample(self, states, actions):
string_actions = []
for action in actions:
string_actions.append(self.features_to_action(action))
return GridworldBase.true_rewards_for_sample(self, states, string_actions)
def true_rewards_for_sample(self, states, actions):
string_actions = []
for action in actions:
string_actions.append(self.features_to_action(action))
return GridworldBase.true_rewards_for_sample(self, states, string_actions)
