Distributed Reinforcement Learning (DRL) is an advanced machine learning method that operates in a distributed system, aiming to optimize how decision-making agents perform in an environment. Traditional reinforcement learning involves an agent making decisions to maximize a cumulative reward through an interactive environment. DRL enhances this process by employing multiple agents across distributed systems which could be hosted on various servers, cloud infrastructure or even multiple cores in a single system.

• Scalability: DRL is heralded for its scalability. As workload increases, additional resources and nodes can be added to improve the handling capacity of the system without bottleneck complaints.
• High Efficiency: By partitioning workload among multiple agents, DRL improves computational speed, making it much more efficient than traditional reinforcement learning mechanisms.
• Greater Stability: Since DRL imparts similar experiences through agents in parallel, it enables good coverage of the state-action space, thereby making learning more stable.
• Asynchronous Training: Unlike traditional reinforcement learning methods that are often limited by sequential training, DRL methods enable distributing the workload for tranquil asynchronous training.
• Extensive Testing: Due to the distributive nature of the system, DRL involves extensive testing under a wide range of conditions, thus enhancing confidence in the models.

Implementing DRL is a systematic effort that requires clearly set objectives, deep understanding of the system's environment and an evaluation of the desired outcomes.

• Need Analysis: Determine specific organizational requirements and assess if DRL is indeed the right fit for achieving the objectives.
• Selecting the Appropriate DRL Model: Study various models and techniques under DRL's umbrella, and select the one that matches the need best.
• Choosing the Implementation Tools: Decide on the framework, the libraries and the tools required for setting up the DRL system.
• Setting Up the Infrastructure: Construct the distributed architecture necessary to run the agents; it could be done either on cloud or on-premise infrastructure.
• Training & Monitoring: Post deployment, continual training and vigilant monitoring of the agents are essential to ensure maximum effectiveness.

DRL is an emerging technology that has the potential to revolutionize many sectors due to its capability to handle large scale, complex problems effectively. Businesses looking for innovative methods to optimize their decision-making processes are turning to DRL, taking advantage of its scalability, efficiency, and adaptive learning capabilities. However, it is necessary to carefully evaluate the specific needs of a business and consider the potential challenges in implementation before deploying a DRL-based solution.

• Expedited Development: As DRL follows an asynchronous training approach, it drastically cuts down the development time, allowing for rapid deployment of solutions.
• Scalability: DRL's inherent trait of scalability makes it flexible to handle increasing or decreasing system loads, aligning with the growth of an organization.
• Efficient Learning: DRL fosters increased efficiency in learning due to its distributed nature, enabling better generalization over the entire environment.
• Collaborative Learning: DRL facilitates collaboration across multiple agents. This encourages collective learning from both unique experiences of each agent and shared experiences across agents.
• Real-time Updates: DRL models are usually designed for continual learning, allowing real-time updates and shifts in strategy.

While the merits of DRL are noteworthy, it also faces some challenges:

• Complex Implementation: As DRL involves coordinated efforts from multiple agents, its implementation can sometimes be challenging.
• Risk of Asynchronous Training: While asynchronous training speeds up the process, if not well managed, it can lead to potential stale gradient updates and consequently, suboptimal models.
• Security Threats: The distributed nature of the system might open up paths for security threats and possible vulnerabilities.
• Integration Challenges: Depending on the used technologies, integrating DRL with preexisting systems may be complex.
• Debugging and Monitoring: Since DRL involves multiple agents working parallelly, debugging and monitoring activities could become a challenging task.

Despite these challenges, DRL remains a highly prevailing approach. The key is to create a careful mapping of the business objectives with DRL capabilities, consider possible limitations and then proceed with implementation.

More light will be thrown upon this topic as more real-world applications of DRL emerge, but suffice to say, that we are on the brink of a technology revolution that is set to reshape many industry domains.