stable_baselines3/mpdqn/mpdqn.py
def predict(
        self,
        observation: np.ndarray,
        state: Optional[np.ndarray] = None,
        episode_start: Optional[np.ndarray] = None,
        deterministic: bool = False,
    ) -> Tuple[np.ndarray, Optional[np.ndarray]]:
        """
        Overrides the base_class predict function to include epsilon-greedy exploration.
        :param observation: the input observation
        :param state: The last states (can be None, used in recurrent policies)
        :param mask: The last masks (can be None, used in recurrent policies)
        :param deterministic: Whether or not to return deterministic actions.
        :return: the model's action and the next state
            (used in recurrent policies)
        """
        return PDQN.predict(self, observation, state, episode_start, deterministic)

        Overrides the base_class predict function to include epsilon-greedy exploration.
        :param observation: the input observation
        :param state: The last states (can be None, used in recurrent policies)
        :param mask: The last masks (can be None, used in recurrent policies)
        :param deterministic: Whether or not to return deterministic actions.
        :return: the model's action and the next state
            (used in recurrent policies)
        
Overrides the base_class predict function to include epsilon-greedy exploration.
Overrides the base_class predict function to include epsilon - greedy exploration .
def predict(
        self,
        observation: np.ndarray,
        state: Optional[np.ndarray] = None,
        episode_start: Optional[np.ndarray] = None,
        deterministic: bool = False,
    ) -> Tuple[np.ndarray, Optional[np.ndarray]]:
        return PDQN.predict(self, observation, state, episode_start, deterministic)
def predict ( self , observation : np . ndarray , state : Optional [ np . ndarray ] = None , episode_start : Optional [ np . ndarray ] = None , deterministic : bool = False , ) -> Tuple [ np . ndarray , Optional [ np . ndarray ] ] : return PDQN . predict ( self , observation , state , episode_start , deterministic )
Overrides the base_class predict function to include epsilon-greedy exploration.
Overrides the base_class predict function to include epsilon - greedy exploration .
"""
        Overrides the base_class predict function to include epsilon-greedy exploration.
        :param observation: the input observation
        :param state: The last states (can be None, used in recurrent policies)
        :param mask: The last masks (can be None, used in recurrent policies)
        :param deterministic: Whether or not to return deterministic actions.
        :return: the model's action and the next state
            (used in recurrent policies)
        """
the model's action and the next state
(used in recurrent policies)
the model ' s action and the next state ( used in recurrent policies )
self
observation
np.ndarray
the input observation
the input observation
state
Optional[np.ndarray]
The last states (can be None, used in recurrent policies)
The last states ( can be None used in recurrent policies )
episode_start
Optional[np.ndarray]
deterministic
bool
Whether or not to return deterministic actions.
Whether or not to return deterministic actions .
mask
The last masks (can be None, used in recurrent policies)
The last masks ( can be None used in recurrent policies )