Probabilistic Reconstruction and Informed Search with Memory for Partially Observable Navigation

Abstract

Model-free reinforcement learning methods for navigation often suffer from poor generalization, sparse rewards, and failures under partial observability. This paper introduces Probabilistic Reconstruction and Informed Search with Memory (PRISM), a hybrid framework that decouples environment understanding from decision-making. Rather than learning a navigation policy end to end, a generative adversarial network (GAN) reconstructs and completes a global map of the environment from partial observations and agent memory. Known observations are strictly preserved, whereas unknown regions are probabilistically inferred to produce plausible world hypotheses consistent with the sensed data. A* search uses the generated map to calculate a minimum-cost path to the target under the planner’s traversal-cost model and replans at each step as new observations update the memory. This separation enables environment reconstruction under limited sensing and interpretable, deterministic planning decisions. Across five estimate-level evaluations per condition, A*+GAN achieved 99.82 ± 0.04% success and averaged 11.26 ± 0.92 steps, compared with 99.60 ± 0.22% success and 12.10 ± 0.09 steps for memory-only A*. A*+GAN had lower all-episode mean steps in all five matched evaluations. The DQN conditions averaged 7.68% to 18.92% success and showed substantially greater between-model variation. Overall, the findings indicate that learning the structure of the world can facilitate effective classical planning without policy memorization, providing an alternative to model-free reinforcement learning for partially observable navigation.