Reinforcement Learning (RL) is a machine learning paradigm that strives to make optimal decision-making based on experience acting in an environment. In many cases, the "environment" refers to a simulator in the training stage and refers to the real world in the deployment stage. Training in the simulator brings a lot of advantages: lower cost, more safety, and more flexibility. However, it is almost impossible to design a perfect simulator that is identical to the real world. Thus, a decision-maker trained in the simulator may not function well in the real world. The discrepancy between the simulator and the real world is called the simulation-to-reality (sim-to-real) gap. This project will build new technologies to close the sim-to-real gap in both the training and the deployment stages. The research outcomes will benefit the development of next-generation RL techniques, which can improve the availability, applicability, and generalization of RL, and minimize the gap of RL between common practices and real-world practices. This project proposes to close the sim-to-real gap in reinforcement learning by three mechanisms: randomization, alignment, and derivation. Specifically, 1) the randomization mechanism generates a set of homogeneous simulators by original simulator parameter randomization. The simulator set will cover a wider range of state-action regions than the original simulator, have a larger overlap with the real-world environment, and thereafter result in a sma