def step()

in envs/thor_beacons.py [0:0]

• 11 lines of code
• 10 McCabe index (conditional complexity)

    def step(self, action):

        obs, rewards, done, info = super().step(action)

        # place beacon for interaction
        if info['action'] in self.interaction_set and info['target'] is not None:
            target = info['target']['objectId'] or info['target']['center_objectId']
            if target is not None:
                self.beacons.append({'t':self.t-1, 'target':target, 'action':info['action'], 'success':info['success']})

        hist = self.compile_history(self.state)
        self.history.append({'t':self.t-1, **hist})

        # if final timestep, then compute masks for the entire trajectory and add to info
        if self.t==self.max_t:
            info['traj_masks'] = self.compute_masks()

        return obs, rewards, done, info