Deep Reinforcement Learning (DRL) is an advanced, cutting-edge tool in the field of artificial intelligence that integrates deep learning and reinforcement learning. It possesses the capacity to mimic human decision-making processes, allowing robots or computer programs to optimize performance and navigate complex environments independently.

DRL exhibits key features which distinguish it from other AI technologies:

• Model-Free Learning: DRL does not require a model of the environment. Instead, it learns directly from raw observations, eliminating the need for extensive feature engineering.

• Adaptive Nature: DRL is adaptive – it becomes better with time. Agents trained using DRL can learn from their mistakes, adapt to new situations, and optimize their performance to achieve maximum rewards with minimal penalties.

• Complex Decision-Making Capability: DRL can handle complex decision-making tasks and can learn optimal strategies from high-dimensional inputs like images and audios.

• Generalization: Unlike Supervised Learning methods that require new training data for every new task, DRL agents can be trained once and deployed in numerous environments due to their ability to generalize from one task to another.

DRL has a diverse range of applications spanning various sectors:

• Gaming: Developers employ DRL to create intelligent, adaptive non-player characters (NPCs) in video games that can learn and improve gameplay strategy over time, thereby challenging human players.

• Robotics: DRL helps create autonomous robots that can navigate complex, uncertain environments and perform delicate industrial tasks with precision.

• Supply Chain: DRL can optimize complex supply chain systems, inventory management, and route planning to maximize efficiency and minimize cost.

• Healthcare: In healthcare, DRL can help personalize treatment plans and accurately predict disease progression.

• Cost-Effective: DRL reduces the need for human intervention, making it a relatively cost-effective solution.

• Proactive Learning: DRL algorithms learn from their environments and proactively seek ways to maximize rewards, thus improving performance over time.

• Independence: DRL trained agents can operate independently without the need for constant supervision.

• Consecutive Learning: DRL algorithms learn by trial and error, systematically reducing errors by learning from past actions.

• Future Predictions: DRL can make accurate predictions about future events based on past data, optimizing decision-making processes.

• Data Hungry: DRL algorithms require a vast amount of data to learn optimally.

• Unpredictability: DRL models may adopt unpredictable behaviors due to their autonomous nature.

• Difficulty in Training: It can be challenging to train DRL models because of their complexity and the balance required between exploration and exploitation.

If the disadvantages are outweighed by the intrinsic benefits and the potential application of DRL aligns with the project's goals, the practical implementation of DRL can begin.

Similar to the implementation of other AI technologies, deploying DRL algorithms requires a clear understanding of the technology and an in-depth evaluation of organizational needs. The goal is to align the capabilities of DRL with the specific needs of the project.

Systematic planning, preparation, careful evaluation and iteration are necessary for a successful DRL implementation. From designing a reward function, preparing the environment, and selecting the right DRL algorithm, to the actual training of the models, and monitoring performance post-deployment, every step of the process demands expert attention and careful handling.

To sum up, Deep Reinforcement Learning offers a revolutionary approach to problem-solving and decision making. The autonomous nature of DRL algorithms combined with their ability to learn from their environment and improve performance over time makes DRL a promising tool for tackling complex tasks across a multitude of sectors.