environment to learn the policy directly however this paper does something different what it does is it learns a policy that is adaptive during training which basically means that at the beginning of each episode the policy in it is initialized randomly and by policy here we mean a policy network uh policy neural network which you can see at the bottom so that's initialized randomly and then during the episode depending on the input uh this network is changed and adapted in order to achieve high performance so even at test time