Discrete World Models via Regularization

Researchers introduce Discrete World Models via Regularization (DWMR), a new method for learning Boolean representations of environments without requiring reconstruction or contrastive learning. The approach uses specialized regularizers to maximize entropy and independence while enforcing locality constraints, showing superior performance on benchmarks with combinatorial structure.