Algorithm 1 Curriculum Offline Imitation Learning (COIL) Require: Offline dataset D, number of trajectories picked at each curriculum N, moving window of the return filter α, number of training iteration L, batch size B, number of pre-train times T, and the learning rate η. Initialize the return filter V = 0. if D is collected by a single policy then Do pre-training for T times using BC. B.1 Proof for Theorem 1 We introduce useful lemmas before providing our proof. Therefore, we have the following proposition. Let Π be the set of all deterministic policy and |Π|= |A||S|.

Ho and we are ver di, f these ficult m to aps create hav and e limitations: maintain, especially create introduce better SN in maps AP an, a automated that deep ca netw n be f ork ashion.

We consider the problem of quantifying uncertainty for the estimation error of the leading eigenvector from Oja's algorithm for streaming principal component analysis, where the data are generated IID from some unknown distribution. By combining classical tools from the U-statistics literature with recent results on high-dimensional central limit theorems for quadratic forms of random vectors and concentration of matrix products, we establish a weighted χ2 approximation result for the sin2 error between the population eigenvector and the output of Ojas algorithm. Since estimating the covariance matrix associated with the approximating distribution requires knowledge of unknown model parameters, we propose a multiplier bootstrap algorithm that may be updated in an online manner. We establish conditions under which the bootstrap distribution is close to the corresponding sampling distribution with high probability, thereby establishing the bootstrap as a consistent inferential method in an appropriate asymptotic regime.

A.1 Molecule Design We present more examples of generated molecules by our method and the CNN baseline liGAN. We select 6 molecules with highest binding affinity for each method and each binding site. The 3 additional binding sites are selected randomly from the testing set. By comparing the samples from two methods, we can find that the 3D molecules generated by our method are generally more realistic, while molecules generated by the baseline have more erroneous structures, such as bonds that are too short and angles that are too sharp. Besides, molecules generated by our method are more diverse, while the 3D atom configurations generated by the baseline are often similar.