Description: Episodic Control is an approach within reinforcement learning that focuses on decision-making through discrete episodes. In this context, an ‘episode’ refers to a complete sequence of interactions between an agent and its environment, starting from an initial state and ending when a terminal state is reached or a specific goal is fulfilled. This method allows the agent to learn from past experiences by evaluating the actions taken and their consequences in each episode. Through the feedback obtained, the agent adjusts its action policy, seeking to maximize the accumulated reward over multiple episodes. The main characteristics of Episodic Control include the ability to learn from the exploration and exploitation of actions, as well as the implementation of algorithms that can be either model-based or model-free. This approach is particularly relevant in environments where decisions must be made in sequences and where the outcome of an action can influence future decisions. In summary, Episodic Control provides a structured framework for learning in dynamic and complex situations, allowing agents to adapt and improve their performance over time.
