Description: A dynamic model in reinforcement learning is an approach used to predict the next state and the reward that will be obtained by taking a specific action in a given state. This type of model is based on the idea that the environment in which a reinforcement learning agent operates can be mathematically represented, allowing the agent to anticipate the consequences of its actions. Dynamic models are fundamental for decision-making, as they enable the agent to plan and optimize its behavior over time. By constructing a model that captures the dynamics of the environment, the agent can simulate different scenarios and evaluate which action maximizes the expected reward. This approach is particularly useful in complex environments where interactions are nonlinear and rewards may be delayed. Dynamic models can be deterministic or stochastic, depending on whether transitions between states are predictable or subject to variability. In summary, dynamic models are powerful tools that allow reinforcement learning agents to learn more efficiently and effectively by providing a structure to understand and anticipate the behavior of the environment.
