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

• URL: http://arxiv.org/abs/2409.01427v5
• Date: Tue, 26 Aug 2025 19:03:23 GMT
• Title: Enhancing Sample Efficiency and Exploration in Reinforcement Learning through the Integration of Diffusion Models and Proximal Policy Optimization
• Authors: Tianci Gao, Konstantin A. Neusypin, Dmitry D. Dmitriev, Bo Yang, Shengren Rao,
• Abstract summary: We present a strictly on policy framework that treats a conditional diffusion model as an adaptable action prior.<n>The prior is pre trained on logged data and used online only at sampling time to propose actions at current on policy states.<n>Results indicate that an adaptable diffusion action prior is a practical way to boost on policy PPO under tight interaction budgets.
• Score: 3.2288603733409498
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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