A Reinforcement Learning Model is a type of Machine Learning model that is getting widespread prevalence in solving complex problems. It gives an agent the ability to learn from the consequences of its own actions within a specific environment, the model's primary target is to find an optimal way to complete a certain task. The essential component of reinforcement learning models is the reward feedback, where a positive reward is used to reinforce good behavior and a negative reward, or punishment, is used to discourage undesirable actions.

• Autonomous Learning: Reinforcement learning models are capable of learning from data without any guidance, they make decisions and learn from the consequences of those decisions.
• Trial and Error approach: Reinforcement learning models leverage the trial and error approach, where an agent performs different actions and analyzes the results to determine which actions led to better results.
• Sequential Decision Making: These models base their decisions on a sequence of steps that lead to a final goal, rather than random actions.
• Reward System: This is the cornerstone of reinforcement learning models, where positive and negative rewards are implemented to guide the learning process.
• Environment Interaction: The models interact directly with the environment, learning how actions impact the environment and the results they generate.
• Balancing act: Reinforcement learning strikes a balance between exploration (trying out new actions to find the best one) and exploitation (using the known best action).

Implementing a reinforcement learning model involves identifying the goal, defining the environment, specifying the reward function, and choosing an appropriate reinforcement learning algorithm. It requires accurate planning, prototyping, regular monitoring, and tweaking to achieve optimal results.

This reinforcement learning journey also involves designing and implementing a policy that guides how the agent will make decisions. The success of the implementation relies heavily on the quality of the decision policy and the appropriateness of the reward function.

Reinforcement learning models have several advantages that make them very attractive for various applications:

• No Need for Pre-labelled Data: Unlike other machine learning models, reinforcement learning models do not require pre-labelled data; they learn directly from interaction with the environment.
• Goal-Oriented Learning: Reinforcement learning models focus on achieving a specific goal, making their learning more directed and efficient.
• Autonomous Decision Making: The models are capable of making their own decisions and learn from them, this grants the model a unique degree of autonomy.
• Versatility: Reinforcement learning models can be used to solve a wide array of complex tasks that involve sequential decision making, ranging from game playing to stock trading.
• Continuous Learning: Reinforcement learning enables continuous learning and adaptability, as new data can be incorporated in the learning process.

Despite the numerous advantages, Reinforcement Learning Models does have certain disadvantages:

• Delayed Reward: In reinforcement learning, the reward can be delayed, and the agent might need to take several steps before obtaining the reward, which can complicate the learning process.
• The Model Complexity: Reinforcement learning models involve complex algorithms and can be quite challenging to set up and troubleshoot.
• Data Requirements: Reinforcement learning models need a vast amount of data to learn correctly and make accurate decisions.
• Dependency on the reward function: The performance of the model is highly reliant on how well the reward function is designed.

In conclusion, reinforcement learning models provide a robust and flexible platform for learning through interaction with the environment and allow for autonomous decision making, which is critical for many applications. However, careful consideration of the disadvantages and effective planning and monitoring are essential for a successful implementation.