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

• URL: http://arxiv.org/abs/2409.01427v3
• Date: Sat, 14 Sep 2024 06:35:02 GMT
• Title: Enhancing Sample Efficiency and Exploration in Reinforcement Learning through the Integration of Diffusion Models and Proximal Policy Optimization
• Authors: Gao Tianci, Dmitriev D. Dmitry, Konstantin A. Neusypin, Yang Bo, Rao Shengren,
• Abstract summary: We propose a framework that enhances PPO algorithms by incorporating a diffusion model to generate high-quality virtual trajectories for offline datasets. Our contributions are threefold: we explore the potential of diffusion models in RL, particularly for offline datasets, extend the application of online RL to offline environments, and experimentally validate the performance improvements of PPO with diffusion models.
• Score: 1.631115063641726
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advancements in reinforcement learning (RL) have been fueled by large-scale data and deep neural networks, particularly for high-dimensional and complex tasks. Online RL methods like Proximal Policy Optimization (PPO) are effective in dynamic scenarios but require substantial real-time data, posing challenges in resource-constrained or slow simulation environments. Offline RL addresses this by pre-learning policies from large datasets, though its success depends on the quality and diversity of the data. This work proposes a framework that enhances PPO algorithms by incorporating a diffusion model to generate high-quality virtual trajectories for offline datasets. This approach improves exploration and sample efficiency, leading to significant gains in cumulative rewards, convergence speed, and strategy stability in complex tasks. Our contributions are threefold: we explore the potential of diffusion models in RL, particularly for offline datasets, extend the application of online RL to offline environments, and experimentally validate the performance improvements of PPO with diffusion models. These findings provide new insights and methods for applying RL to high-dimensional, complex tasks. Finally, we open-source our code at https://github.com/TianciGao/DiffPPO

Related papers

• D5RL: Diverse Datasets for Data-Driven Deep Reinforcement Learning [99.33607114541861]
  We propose a new benchmark for offline RL that focuses on realistic simulations of robotic manipulation and locomotion environments. Our proposed benchmark covers state-based and image-based domains, and supports both offline RL and online fine-tuning evaluation.
  arXiv  Detail & Related papers  (2024-08-15T22:27:00Z)
• Hybrid Reinforcement Learning for Optimizing Pump Sustainability in Real-World Water Distribution Networks [55.591662978280894]
  This article addresses the pump-scheduling optimization problem to enhance real-time control of real-world water distribution networks (WDNs) Our primary objectives are to adhere to physical operational constraints while reducing energy consumption and operational costs. Traditional optimization techniques, such as evolution-based and genetic algorithms, often fall short due to their lack of convergence guarantees.
  arXiv  Detail & Related papers  (2023-10-13T21:26:16Z)
• Reparameterized Policy Learning for Multimodal Trajectory Optimization [61.13228961771765]
  We investigate the challenge of parametrizing policies for reinforcement learning in high-dimensional continuous action spaces. We propose a principled framework that models the continuous RL policy as a generative model of optimal trajectories. We present a practical model-based RL method, which leverages the multimodal policy parameterization and learned world model.
  arXiv  Detail & Related papers  (2023-07-20T09:05:46Z)
• Look Beneath the Surface: Exploiting Fundamental Symmetry for Sample-Efficient Offline RL [29.885978495034703]
  offline reinforcement learning (RL) offers an appealing approach to real-world tasks by learning policies from pre-collected datasets. However, the performance of existing offline RL algorithms heavily depends on the scale and state-action space coverage of datasets. We provide a new insight that leveraging the fundamental symmetry of system dynamics can substantially enhance offline RL performance under small datasets.
  arXiv  Detail & Related papers  (2023-06-07T07:51:05Z)
• Robust Reinforcement Learning Objectives for Sequential Recommender Systems [7.44049827436013]
  We develop recommender systems that incorporate direct user feedback in the form of rewards, enhancing personalization for users. employing RL algorithms presents challenges, including off-policy training, expansive action spaces, and the scarcity of datasets with sufficient reward signals. We introduce an enhanced methodology aimed at providing a more effective solution to these challenges.
  arXiv  Detail & Related papers  (2023-05-30T08:09:08Z)
• Federated Deep Reinforcement Learning for the Distributed Control of NextG Wireless Networks [16.12495409295754]
  Next Generation (NextG) networks are expected to support demanding internet tactile applications such as augmented reality and connected autonomous vehicles. Data-driven approaches can improve the ability of the network to adapt to the current operating conditions. Deep RL (DRL) has been shown to achieve good performance even in complex environments.
  arXiv  Detail & Related papers  (2021-12-07T03:13:20Z)
• Behavioral Priors and Dynamics Models: Improving Performance and Domain Transfer in Offline RL [82.93243616342275]
  We introduce Offline Model-based RL with Adaptive Behavioral Priors (MABE) MABE is based on the finding that dynamics models, which support within-domain generalization, and behavioral priors, which support cross-domain generalization, are complementary. In experiments that require cross-domain generalization, we find that MABE outperforms prior methods.
  arXiv  Detail & Related papers  (2021-06-16T20:48:49Z)
• Offline Reinforcement Learning from Images with Latent Space Models [60.69745540036375]
  offline reinforcement learning (RL) refers to the problem of learning policies from a static dataset of environment interactions. We build on recent advances in model-based algorithms for offline RL, and extend them to high-dimensional visual observation spaces. Our approach is both tractable in practice and corresponds to maximizing a lower bound of the ELBO in the unknown POMDP.
  arXiv  Detail & Related papers  (2020-12-21T18:28:17Z)
• Critic Regularized Regression [70.8487887738354]
  We propose a novel offline RL algorithm to learn policies from data using a form of critic-regularized regression (CRR) We find that CRR performs surprisingly well and scales to tasks with high-dimensional state and action spaces.
  arXiv  Detail & Related papers  (2020-06-26T17:50:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.