In this work, we build on the de novo molecular design platform, REINVENT, by adapting curriculum learning (CL) to accelerate agent convergence on complex multi-parameter optimization (MPO) objectives. Relative to baseline reinforcement learning (RL) which may issue many non-productive calls to expensive physics-based descriptors, simple curricula consisting of even one Curriculum Objective can successfully guide the agent to achieve productivity in substantially reduced time. We demonstrate the application of CL on two Production Objectives: Constructing a relatively complex scaffold and satisfying a molecular docking constraint. In the former, given the same number of epochs, CL successfully constructs the complex structure from simpler constituents while baseline RL is unsuccessful. In the second application example, using Tanimoto (2D) or ROCS (3D) shape similarity to the reference ligand as Curriculum Objectives guides the agent to areas of chemical space that satisfies the docking constraint. In contrast, baseline RL significantly struggles, spending many epochs generating unfavourable compounds. CL facilitates direct steering of agent policy towards a Production Objective by providing the ability to teach the agent specific knowledge. The results show that teaching the agent to optimize Curriculum Objectives to a greater degree can enhance the ability to satisfy a complex Production Objective, relative to baseline RL. However, optimizing similarity-based Curriculum Objectives to a greater degree leads to lower intra-set diversity, as the agent generates compounds that are 'closer' in chemical space. Thus, devising appropriate curricula allows one to accelerate agent convergence and steer agent policy update for bespoke applications. , where 𝑥 is a sampled compound in the SMILES format and 𝑐 𝑖 : SMILES → [0, 1] and 𝑤 𝑖 are the 𝑖th component and its corresponding weighting, respectively. 𝑆(𝑥) computes the desirability of the sampled compound, 𝑥, and its corresponding gradient is used to update the agent policy. Curriculum Phase. In the Curriculum Phase, the goal is for the agent to learn to generate compounds that satisfy sequential Curriculum Objectives with increasing complexity that guide the agent towards the Production Phase. 𝑂 𝐶 1 , … , 𝑂 𝐶 𝑛-1 , 𝑂 𝐶 𝑛 are designated Curriculum Objectives with corresponding Curriculum Progression Criterions 𝑃 1 , … , 𝑃 𝑛-1 , 𝑃 𝑛 , that enforces sufficient agent learning of each sequential Curriculum Objective based on a score threshold. If the score threshold is met, the agent progresses to the sequential Curriculum Objective, otherwise, the agent continues learning the current Curriculum Objective. This process collectively constitutes the Curriculum Phase.