This research explores Intrinsic Credit Assignment, a framework designed to help artificial agents learn in complex, long-horizon environments without constant external feedback. The text details various interactive simulations, such as "Twenty Questions," "Guess My City," and "Murder Mystery," where agents must use strategic inquiry to achieve specific goals. Each task utilizes a judge-and-questioner dynamic to test the agent’s ability to refine its internal beliefs and solve problems through natural language dialogue. By simulating roles like customer service representatives or detectives, the study evaluates how well models handle uncertainty and sequential reasoning. Ultimately, the framework aims to develop more autonomous learners capable of navigating intricate real-world scenarios through internal progress evaluation.