Model-based reinforcement learning (RL) is a type of machine learning approach where an agent learns how to make decisions by interacting with an environment. In model-based RL, the agent builds a model of the environment to predict future states and the outcomes of its actions. This model can be used to plan actions more efficiently compared to model-free methods, where the agent learns only from direct interaction and trial and error. The main idea behind model-based RL is to leverage this internal model to improve learning and decision-making.
In practice, a model-based RL process typically consists of two primary components: a model of the environment and a planning algorithm. The model can be used to estimate the dynamics of the environment, such as how actions will affect the state or what rewards will be received. For instance, imagine a robot navigating a maze; the model would help it predict which paths lead to the goal and which paths result in obstacles. After building and refining the model through interactions, the agent can use planning techniques—like dynamic programming or Monte Carlo tree search—to simulate different action sequences and select the best one based on expected long-term rewards.
One of the advantages of model-based RL is its sample efficiency. Because the agent can run simulations using the learned model, it can explore potential states and actions without needing to experience every possible scenario in reality. For example, in a game-playing environment like chess, the agent can simulate various moves without actually playing each game out. However, building an accurate model can be challenging, and inaccuracies can lead to poor decision-making. Balancing model accuracy and computational efficiency is a critical aspect of implementing model-based RL effectively. In summary, model-based RL stands out by enhancing learning through the predictive power of a learned model, allowing for more informed and strategic decision-making.