Model-based reinforcement learning (MBRL) is a class of algorithms in the field of reinforcement learning (RL) that utilizes a model of the environment to make predictions about future states and rewards. This approach contrasts with model-free reinforcement learning, which learns policies and value functions directly from interactions with the environment without an explicit model. MBRL