While human presence in cislunar space continues to expand, so too does the demand for 'lightweight' automated on-board processes. In nonlinear dynamical environments, computationally efficient guidance strategies are challenging. Many traditional approaches rely on either simplifying assumptions in the dynamical model or abundant computational resources. The proposed controller employs the use of the nonlinear equations of motion without imposing a heavy workload on a flight computer. The guidance framework is nevertheless able to leverage high-performance computing by separating the training from the resulting controller. Practical examples demonstrate the flexibility of a reinforcement learning approach, and suggest extendability to higher-fidelity domains.
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