def reset(self, mode=0):
        self.mode=mode
        
        self._last_observation = self.env.reset()
        if (self.mode==-1): # Reset to a random value when in training mode (that allows to increase exploration)
            high=self.env.observation_space.high
            low=self.env.observation_space.low
            self._last_observation=low+self.rng.rand(2)*(high-low)            
            self.env.state=self._last_observation

        self.isTerminal=False
        

        return self._last_observation
Reset environment for a new episode.
dataset/ETHPy150Open VinF/deer/examples/gym/mountain_car_env.py/MyEnv.reset