Partially observable environments present increased decision-making complexity for Reinforcement Learning agents. This paper explores methods for improving automated decision-making in partially observable environments by utilizing Variational Auto-Encoders (VAEs) trained on expert demonstrations to generate intrinsic rewards for reinforcement learning (RL) agents. Specifically, a VAE is pre-trained on expert demonstrations to construct a latent representation of successful decision-making. This latent representation is utilized to generate intrinsic rewards via KL-divergence, augmenting the extrinsic reward signal during Proximal Policy Optimization (PPO) training. Experiments are conducted using a symbolic-matching navigation task within a Unity ML-Agents environment, which requires memory formation and hierarchical reasoning. Results indicate that incorporating demonstration-based intrinsic rewards improves the learning efficiency and convergence of PPO agents compared to baseline models without intrinsic rewards. The findings suggest that latent-space representations from demonstrations can effectively guide exploration in challenging RL scenarios, and that certain behaviors are retained from demonstrations.