# Understanding policy update in PPO2

I have a question regarding the functionality of the PPO2 algorithm together with the Stable Baselines implementation:

From the original paper I know that the policy parameters $$\theta$$ are updated K-times using the steps sampled (n_env * T steps):

When updating the policy parameters for a state $$s_t$$, are only the state observations $$a_t$$ and reward $$r_{t+1}$$ of this step considered, or also the state observations and rewards of the following steps ($$t+1$$) considered? My understanding is that the policy update with stochastic gradient ascent works just like in supervised learning.

I know that PPO2 uses a truncated TD($$\lambda$$) approach (T timesteps considered). So I guess that during the policy update for each state, subsequent states are only considered through the advantage function $$A_t$$ but not through the values of subsequent state observations and rewards themselves? Is that true?

I do not quite get the Stable Baselines implementation in the method _train_step() of the PPO2 implementation so therefore the question here.