This paper addresses the problem of scheduling real-time wireless flows with general traffic patterns in dynamic network conditions. The main goal is to maximize the fraction of packets to be delivered within their deadlines, which is referred to as timely-throughput. While scheduling algorithms for frame-based traffic models and greedy maximal scheduling methods like LDF have been thoroughly studied, algorithms providing deadline guarantees on packet delivery for general traffic under dynamic network conditions are insufficient. To address this issue, we present a comparative study of two deep reinforcement learning-based scheduling algorithms: RL-Centralized and RL-Decentralized, which are designed to optimize timely-throughput for real-time wireless flows with general traffic patterns in dynamic wireless networks. The RL-Centralized scheduling algorithm formulates the centralized scheduling problem as a Markov Decision Process (MDP) and leverages a Multi-Environments Dueling Double Deep Q-Network (ME-D3QN) structure to adapt to dynamic network conditions. The RL-Decentralized scheduling problem is formulated as a Multi-Agent Markov Decision Process (MMDP) and employs the Node State Consensus Protocol (NSCP) and Lifelong Reinforcement Learning Decentralized Training and Decentralized Execution (LRL-DTDE) structure to accelerate training. Our experimental results indicate that both proposed algorithms can converge quickly and efficiently adapt to dynamic network conditions with better performance than their baseline policies. Finally, test-bed experiments validate simulation results and confirm that the proposed algorithms are practical on resource-limited platforms.