Conventional topology optimization frameworks efficiently minimize structural compliance but rely on homogenized material laws that assume a clear separation of length scales and restrict local material adaptivity. These assumptions often limit achievable performance and fail to account for manufacturability and spatial dependencies efficiently. To overcome these challenges, we propose a topology optimization framework extended by Deep Reinforcement Learning (DRL) that enables context-aware, element-wise adaptation of material properties under global constraints. The DRL agent performs policy inference on local strain fields to determine optimal material parameters like fiber orientation, volume fraction, and density, which are directly mapped to the element stiffness tensor. This allows the structural response to emerge from full-scale analysis without relying on homogenization assumptions or restrictive parameterizations. The reward formulation embeds manufacturability and mass constraints, including a discrete set of admissible fiber orientations, penalties for abrupt local transitions, and a global mass budget. Once trained, the agent generalizes across unseen topologies and loading conditions, providing efficient, context-aware material adaptation during inference without the computational burden of repeated FE2 homogenization. Building upon earlier work on reinforcement learning for material optimization the proposed framework bridges the gap between topology and material design, extending classical optimization toward manufacturability-aware, context sensitive, and spatially adaptive structures.