CONDITIONAL GENERATIVE MODELING FOR DECISION MAKING

Abstract

In this review, we examine the chance of applying restrictive generative models to successive navigation,
a task that reinforcement learning (RL) has generally been liable for. Late improvements in restrictive generative
displaying have shown promising results in delivering top notch pictures, which has driven us to investigate their
likely use in dynamic methods. Through the viewpoint of contingent generative displaying, our technique takes a
gander at successive direction and proposes that these models could give strategies that boost returns, perhaps
getting rid of the need for support learning. We use a choice diffuser to apply restrictive generative demonstrating to
consecutive independent direction. To empower the model to meet numerous imperatives during testing, it is
prepared utilizing molding on a solitary requirement or expertise. This approach shows that dynamic utilizing
restrictive generative models and disconnected reinforcement learning is conceivable. In dynamic errands,
restrictive generative models can effectively supplant reinforcement learning (RL) by demonstrating a procedure
that enhances returns. Our outcomes give a new perspective on successive direction and exhibit the versatility and
power of restrictive generative models in producing the most ideal strategies under a scope of impediments and
conditions.