The article explores the challenges of policy-gradient methods in long-horizon decision-making, particularly in scenarios where immediate actions may lead to long-term negative outcomes. It identifies issues of completion and optimality, proposing solutions tested in two distinct environments, with findings that could influence future AI decision-making strategies in complex scenarios.