Enhancing Sample Efficiency and Exploration in Reinforcement Learning through the Integration of Diffusion Models and Proximal Policy Optimization

On policy reinforcement learning (RL) methods such as PPO are attractive for continuous control but suffer from poor sample efficiency in costly, high dimensional settings. We present a strictly on policy framework that treats a conditional diffusion model as an adaptable action prior rather than a policy or world model. The prior is pre trained on logged data and used online only at sampling time to propose actions at current on policy states. Two lightweight mechanisms - value guided proposal generation (energy re weighting and in process gradient guidance) and a soft prior KL - regularize the actor via a small auxiliary imitation loss while keeping all PPO updates strictly on on-policy rollouts. To adapt the prior without heavy compute, we apply parameter efficient tuning (PET) that updates only adapters/LoRA, yielding a dual proximal view: policy KL is constrained by PPO and prior KL by PET. Across eight MuJoCo tasks under a shared 1.0M step budget, our method improves early learning (ALC@40) in 3/4 settings and matches or exceeds final return on 6/8 tasks with only 15-30% wall clock overhead. Ablations show that freezing the prior degrades performance and removing value guidance slows early learning; t SNE analyses confirm that value guidance concentrates proposals in high Q regions. Results indicate that an adaptable diffusion action prior is a practical way to boost on policy PPO under tight interaction budgets.