Autonomous agents operating in complex environments must constantly balance safety and efficiency while also being equipped to adapt to changing variables. Existing methods, such as reinforcement learning, can be too risky to an agent’s safety to allow for adaptation, while frameworks utilising simulations can be too computationally demanding for practical deployment. This current research explores how agents that create and use learnt expectations from internal simulations can increase their safety, adaptability, and efficiency in complex environments.