Reinforcement Learning State Prediction for Adaptive Planning

The patent combines the environment model and planning model into a single integrated neural network architecture. The shared layers learn common features from observations, while separate heads generate environment predictions and planning values. This merging ensures both models are trained jointly on the same data and objectives, eliminating the mismatch problem while maintaining reasonable architectural organization.

The neural network is designed with universal shared layers that process observations and generate features used by both the environment model and planning model. This multi-functional design allows the same learned representations to serve dual purposes: predicting environmental dynamics and evaluating planning outcomes, ensuring consistency across both functions.

The system employs self-supervised learning where the model generates its own training signals through consistency objectives. The planning model predicts future states and rewards, and these predictions are used to create training targets without requiring external labeled data. The model learns from the structure of the planning task itself, making the training process self-sufficient and eliminating dependency on large labeled datasets.

The system performs preliminary unsupervised pre-training on raw observations to learn environmental dynamics and feature representations before fine-tuning on specific planning tasks. This two-stage approach allows the model to accumulate general knowledge from unlabeled data first, then adapt to specific tasks with minimal labeled examples, significantly reducing overall data requirements.