def single_step()

in ppo_ewma/roller.py [0:0]

• 19 lines of code
• 4 McCabe index (conditional complexity)

    def single_step(self, **act_kwargs) -> dict:
        """
        step vectorized environment once, return results
        """
        out = {}
        lastrew, ob, first = tree_map(tu.np2th, self._venv.observe())
        if self._keep_cost:
            out.update(
                lastcost=tu.np2th(
                    np.array([i.get("cost", 0.0) for i in self._venv.get_info()])
                )
            )
        ac, newstate, other_outs = self._act_fn(
            ob=ob, first=first, state_in=self._state, **act_kwargs
        )
        self._state = newstate
        out.update(lastrew=lastrew, ob=ob, first=first, ac=ac)
        self._venv.act(tree_map(tu.th2np, ac))
        for (k, v) in other_outs.items():
            out[k] = v
        self._step_count += 1
        return out