Open-Radio Access Network (O-RAN) facilitates the scalability of cellular networks by introducing a RAN Intelligent Controller (RIC) component whose functions can be flexibly distributed over large-scale 6G networks. Artificial Intelligence (AI) is effective in optimizing RIC placement in 6G O-RAN, mitigating the limited adaptability of non-data-driven methods in complex time-varying network conditions. However, the cen- tralized orchestration of current approaches for RIC placement hinders scalability. This work introduces a data-driven DE- centralized Reinforced RAN Intelligent Controller orchestration (DERRIC) method for 6G networks, leveraging the online learning capabilities of decentralized multi-agent Reinforcement Learn- ing (RL) orchestration to solve the RAN Intelligent Controller Placement Problem (CPP). DERRIC is a two-layer network management scheme with decentralized orchestrators that adapt to network conditions, deploy controllers, and allocate resources. These orchestrators manage distributed controllers to optimize RAN parameters, such as user transmission power. DERRIC’s main goal is to increase the system’s overall user Packet Delivery Ratio (PDR) by optimal controller deployment and operation. Optimal controller deployment reduces controller-user latency and accelerates user-transmission-power control decisions, leading to further enhancement to user PDR. We show that DERRIC reduces the controller-user latency and power consumption by up to 66% and 29% and increases user PDR by up to 14% compared to state-of-the-art baselines in a broad range of simulated scenarios.