Temporal Difference Learning, or TD Learning, is a prominent model-free reinforcement learning method, widely utilized in the field of machine learning and artificial intelligence. This method learns from the approximation of the difference, or the 'temporal difference,' between the predictions of the current and next step. It brilliantly combines ideas from Monte Carlo and Dynamic Programming methods and is ideally suited to continuous tasks or tasks within unknown environments.

• Online/Incremental Learning: TD Learning facilitates learning while the agent experiences the world in small increments. Unlike Monte Carlo methods that wait until the outcome of an episode before learning, TD Learning can update its estimates and learn at each step.
• Bootstrap Approach: Just like Dynamic Programming, TD Learning leverages the knowledge of previous approximations to update the estimate of successive states, following an approach identified as 'bootstrap.'
• Reward-Based: TD Learning is based on the principle that the agent learns by receiving rewards or penalties for its actions, improving its policy towards the target goal.

Temporal Difference Learning's implementation necessitates a diligent understanding of the problem at hand, the environment, and the possible actions an agent can take. The TD Learning algorithm is initialized with arbitrary values for state or action-values. The agent interacts with the environment and learns from the outcome of its actions, capturing the differences between the expected and actual reward to update the state or action-value approximations.

Performance of TD Learning algorithm greatly depends on the selection of the learning rate and discount factor. The learning rate identifies the degree to which new information overrides old, while the discount factor modulates the importance given to future rewards. Therefore, a careful evaluation of these parameters is necessary for the successful implementation of TD Learning.

Additional factors such as exploration strategy also play a critical role. Common strategies include ε-greedy (where the agent periodically takes random actions to explore) and Softmax (where action selection is proportional to expected reward). Depending upon the application, one might be more suitable than the other.

The landscape of Temporal Difference Learning is vast and holds various widely recognized algorithms like Q-learning and SARSA, which can provide a good starting point for implementation. Before deciding on the most suitable algorithm for specific tasks, an in-depth understanding of these algorithms is recommended. Researching the literature, consulting domain experts, and testing various approaches through experimentation can ensure an effective implementation.

• Reduced Computation: TD Learning utilizes bootstrapping, which reduces the quantity of computation needed as it does not necessitate the execution of a task until the end, or waiting to experience the full reward; thus, it proves more efficient.
• Operational in Unknown Environments: TD Learning operates excellently in environments where an exact model of the environment is unknown or challenging to derive.
• Balancing Exploration & Exploitation: TD Learning is capable of wisely balancing the trade-off between exploration (trying new actions) and exploitation (leveraging profitable actions).
• Continuous Learning: TD Learning enables continuous learning with each step making it suitable for continuous tasks; it does not require the completion of an episode or task before learning.
• Handling Temporal Credit Assignment: TD Learning effectively manages the Temporal Credit Assignment issue, allowing the appropriate assignment of credit to actions aiding in reaching a goal.

• Dependent on Initial State Values: TD Learning methods, due to their bootstrap approach, are profoundly influenced by the accuracy of initial state values.
• Volatile to Changes: Interdependent nature of TD Learning makes it sensitive to changes in the compared state; thus, alterations in one state could lead to modifications in values of several other chained states.
• Difficulties in Convergence: It can be challenging to achieve convergence in non-linear function approximations through TD Learning methods.

In conclusion, despite some potential drawbacks, Temporal Difference Learning has proven to be highly efficient in reinforcement learning tasks. Its advantages and capabilities have been shown in various applications ranging from game playing to robotics. However, the optimal adoption of TD Learning is contingent upon careful consideration, diligent planning, and methodical execution.