The paper introduces SERL (Sample-Efficient Robotic Reinforcement Learning), a comprehensive software suite designed to facilitate the use of reinforcement learning (RL) in real-world robotic tasks. SERL addresses the challenges of implementation details, reward specification, environment resets, and controller design, which are often as important as the choice of algorithm for achieving successful RL. The suite includes an efficient off-policy deep RL method, reward specification methods, a forward-backward controller for reset-free training, and a high-quality controller for robotic manipulation. Experimental results demonstrate that SERL can achieve near-perfect success rates in tasks such as PCB board insertion, cable routing, and object relocation within 25 to 50 minutes of training per policy, outperforming state-of-the-art methods with fewer demonstrations. The authors aim to lower the barrier to entry for researchers and practitioners by providing a well-designed, open-source framework that can be adapted to various robotic systems and tasks.The paper introduces SERL (Sample-Efficient Robotic Reinforcement Learning), a comprehensive software suite designed to facilitate the use of reinforcement learning (RL) in real-world robotic tasks. SERL addresses the challenges of implementation details, reward specification, environment resets, and controller design, which are often as important as the choice of algorithm for achieving successful RL. The suite includes an efficient off-policy deep RL method, reward specification methods, a forward-backward controller for reset-free training, and a high-quality controller for robotic manipulation. Experimental results demonstrate that SERL can achieve near-perfect success rates in tasks such as PCB board insertion, cable routing, and object relocation within 25 to 50 minutes of training per policy, outperforming state-of-the-art methods with fewer demonstrations. The authors aim to lower the barrier to entry for researchers and practitioners by providing a well-designed, open-source framework that can be adapted to various robotic systems and tasks.