A comparative analysis of reinforcement learning algorithms for earth-observing satellite scheduling

Deep reinforcement learning (DRL) has shown promise for spacecraft planning and scheduling due to the lack of constraints on model representation, the ability of trained policies to achieve optimal performance with respect to a reward function, and fast execution times of the policies after training. Past work investigates various problem formulations, algorithms, and safety methodologies, but a comprehensive comparison between different DRL methods and problem formulations has not been performed for spacecraft scheduling problems. This work formulates two Earth-observing satellite (EOS) scheduling problems with resource constraints regarding power, reaction wheel speeds, and on-board data storage. The environments provide both simple and complex scheduling challenges for benchmarking DRL performance. Policy gradient and value-based reinforcement learning algorithms are trained for each environment and are compared on the basis of performance, performance variance between different seeds, and wall clock time. Advantage actor-critic (A2C), deep Q-networks (DQN), proximal policy optimization (PPO), shielded proximal policy optimization (SPPO) and a Monte Carlo tree search based training-pipeline (MCTS-Train) are applied to each EOS scheduling problem. Hyperparameter tuning is performed for each method, and the best performing hyperparameters are selected for comparison. Each DRL algorithm is also compared to a genetic algorithm, which provides a point of comparison outside the field of DRL. PPO and SPPO are shown to be the most stable algorithms, converging quickly to high-performing policies between different experiments. A2C and DQN are typically able to produce high-performing policies, but with relatively high variance across the selected hyperparameters. MCTS-Train is capable of producing high-performing policies for most problems, but struggles when long planning horizons are utilized. The results of this work provide a basis for selecting reinforcement learning algorithms for spacecraft planning and scheduling problems. The algorithms and environments used in this work are provided in a Python package called bsk_rl to facilitate future research in this area.