def reset()

in rlstructures/env_wrappers/gymenv.py [0:0]

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

    def reset(self, env_info=DictTensor({})):
        assert env_info.empty() or env_info.n_elems() == self.n_envs()
        N = self.n_envs()
        reward = torch.zeros(N)

        last_action = None
        if isinstance(self.gym_envs[0].action_space, gym.spaces.Discrete):
            last_action = torch.zeros(N, dtype=torch.int64)
        else:
            a = self.gym_envs[0].action_space.sample()
            a = torch.tensor(a).unsqueeze(0).repeat(N, 1)
            last_action = a

        done = torch.zeros(N).bool()
        initial_state = torch.ones(N).bool()
        self.env_info = env_info
        frames = None
        if env_info.empty():
            frames = [format_frame(e.reset()) for e in self.gym_envs]
        else:
            frames = []
            for n in range(len(self.gym_envs)):
                v = {k: env_info[k][n].tolist() for k in env_info.keys()}
                frames.append(format_frame(self.gym_envs[n].reset(env_info=v)))
        _frames = []
        for f in frames:
            if isinstance(f, torch.Tensor):
                _frames.append({"frame": f})
            else:
                _frames.append(f)
        frames = [DictTensor(_f) for _f in _frames]
        frames = DictTensor.cat(frames)
        frames.set("reward", reward)
        frames.set("done", done)
        frames.set("initial_state", initial_state)
        frames.set("last_action", last_action)
        return frames, torch.arange(N)