Multi-Agent Reinforcement Learning (MARL) is a rapidly growing concept within the artificial intelligence and machine learning domains. It allows numerous autonomous agents to learn how to reciprocate optimally in an environment to maximize the cumulative reward, relying on the holistic understanding of the environment dynamics and interactions between agents. These agents learn independently yet collectively, influencing each other's learning process.

• Multi-Agent Environment: Unlike single-agent learning that involves a sole entity's interaction with the environment, MARL encompasses multiple entities or agents that learn within the same environment concurrently.
• Independent and Co-Learning: Each agent learns independently by impacting and learning from others' actions in the common environment.
• Collaborative and Competitive: Whether agents collaborate, compete, or simultaneously do both in a shared environment, they adapt by learning from their mistakes and adjust strategies to maximize their respective rewards.
• Complexity: The multitude of interacting agents generally amplifies complexity in MARL due to stochastic behaviors, requiring advanced computational resources and sophisticated algorithms.
• Heterogeneity: The agents involved may exhibit diverse capabilities, thus creating a heterogeneous environment that must inter-operate to resolve complex tasks.
• Distributed Control: MARL systems often engage in distributed control as each agent independently decides its moves based on the spatial-temporal environment and actions of its counterparts.

Successful MARL execution relies on meticulous planning, understanding the agents' functionalities, and the creation of an environment that allows active learning. An agent must be capable of learning continuously, updating its knowledge based on its past experiences, and adjusting its strategies when facing new situations.

Although there are complexities involved in MARL, finding optimal learning methods, knowing when to update agents and their policies, devising methods for reward distribution, and knowing when to exploit or explore can drive successful MARL implementation. The advanced capabilities and benefits make MARL a prominent player in the future of artificial intelligence and machine learning, addressing many modern challenges.

• High Efficiency: Due to the collaborative or competitive nature of multi-agent reinforcement learning, systems usually fetch higher efficiency and reliability.
• Robustness: Forming a robust solution, it is resilient to the failing of a subset of agents, given the distributed learning process.
• Scalability: The distributed nature of the MARL allows for high scalability, making the system exceptionally versatile to accommodate new agents with different roles or attributes.
• Problem Solving: MARL models can resolve complex tasks requiring cooperation, coordination, or competition between multiple decision-making agents.
• Real-World Application: They bear resemblance to complex real-world scenarios involving multiple decision-makers, making them ideal for applications like traffic light control, robotic swarm, and automated trading, which usually involve numerous interacting entities aiming to achieve individual or shared objectives.

• Non-Stationary Environment: The primary disadvantage of MARL scenarios arises from the non-stationary distribution that prompts the risk of non-convergence and instability in learning.
• Complexity: The MARL field suffers from increased complexity given the multiple active agents, non-stationary environment, and co-adaptation difficulties.
• Decision-Making: The quantity of autonomous agents can potentially lead to a 'curse of dimensionality' problem due to numerous state-action pairs.
• Network Overhead: Implementing MARL models involves considerable communication costs amongst agents that may lead to network overhead.
• Computational Intensity: The application often necessitates high computational resources due to the complexity.

Despite these issues, appropriate countermeasures like using efficient MARL algorithms can mitigate such disadvantages in favour of its beneficial aspects.