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Diffusion Models for Reinforcement Learning: A Survey

Zhengbang Zhu, Hanye Zhao, Haoran He, Yichao Zhong, Shenyu Zhang, Haoquan Guo, Tingting Chen, Weinan Zhang

arXiv:2311.01223v4cs.LGcs.AI

TL;DR

RL methods face challenges including limited policy expressiveness, data scarcity, compounding planning errors, and weak multitask generalization. This survey reviews diffusion models' roles and applications in RL, concluding that they address several of these longstanding challenges while the field remains emerging.

  • Problem

    RL faces restricted policy expressiveness, data scarcity, compounding errors in model-based planning, and difficulty generalizing across tasks.

    Source: §1 · p. 1

  • Method

    The survey reviews RL challenges, categorizes diffusion-based methods by their roles in RL, and covers related applications and future research directions.

    Source: §1 · pp. 1–2, §4 · p. 4

  • Results

    The survey concludes that diffusion models address several longstanding RL challenges, including restricted expressiveness, data scarcity, compounding error, and multitask generalization.

    Source: §6 · p. 8

  • Takeaways & Limitations

    Diffusion models in RL remain an emerging field with research topics still worth exploring.

    Source: §6 · p. 8

    Online RL applications are less explored than offline settings and merit further investigation.

    Source: §5.2 · p. 8

Abstract

from arXiv · show

Diffusion models surpass previous generative models in sample quality and training stability. Recent works have shown the advantages of diffusion models in improving reinforcement learning (RL) solutions. This survey aims to provide an overview of this emerging field and hopes to inspire new avenues of research. First, we examine several challenges encountered by RL algorithms. Then, we present a taxonomy of existing methods based on the roles of diffusion models in RL and explore how the preceding challenges are addressed. We further outline successful applications of diffusion models in various RL-related tasks. Finally, we conclude the survey and offer insights into future research directions. We are actively maintaining a GitHub repository for papers and other related resources in utilizing diffusion models in RL: https://github.com/apexrl/Diff4RLSurvey.

1 Introduction

Diffusion models offer high-quality sample generation and training stability, while RL still faces challenges in expressive policies, data scarcity, planning errors, and multitask generalization. The survey reviews diffusion-based approaches to these challenges and related tasks, organizing its coverage around RL challenges, model foundations, roles, applications, and future directions.

1 Introduction

  • Diffusion models reverse a multistep noising process and have shown high sample quality and training stability relative to earlier generative models.The survey notes applications across computer vision, language, audio, and drug discovery.Source: §1 · p. 1
  • RL faces restricted Gaussian-policy expressiveness on complex offline datasets, data scarcity, compounding errors in autoregressive planning, and difficulty generalizing across tasks.These challenges persist despite offline learning and experience replay being used to address sample efficiency.Source: §1 · p. 1
  • Diffusion models are being applied in sequential decision-making, especially offline RL, as trajectory planners, policies, dataset augmenters, and for broader tasks such as multitask RL and imitation learning.Diffuser [Janner et al., 2022] generates and guides trajectories from offline data.Source: §1 · p. 1
  • The survey examines RL challenges and diffusion foundations, then categorizes methods by their roles, reviews RL-related applications, and discusses emerging research topics.Its scope also includes trajectory generation and imitation learning because of their interrelations with RL.Source: §1 · pp. 1–2

2 Challenges in Reinforcement Learning

The survey identifies four persistent RL challenges: fitting complex offline data, obtaining sufficient experience, limiting model-planning errors, and generalizing across tasks. It explains how diffusion models' distributional modeling can address these issues.

2.1 Restricted Expressiveness in Offline Learning

  • Diffusion models can represent multimodal offline data, potentially improving policy-constraining and return-conditioned supervised-learning approaches that must fit the dataset.Policy constraints with unimodal Gaussian policies may struggle with diverse data; return-conditioned supervised learning avoids Q-learning but also must fit the dataset.Source: §2.1 · p. 2

2.2 Data Scarcity in Experience Replay

  • Replay data may be insufficient for policy optimization, while offline learning is limited by dataset quality and coverage; prior augmentation can lack fidelity or modeling capacity.Diffusion models' generative capacity is presented as better suited to augmenting complex interactions.Source: §2.2 · p. 2

2.3 Compounding Error in Model-based Planning

  • Cumulative single-step prediction errors can push planned states outside the dataset distribution, whereas trajectory-level diffusion planning handles multiple time steps jointly.The survey presents joint trajectory modeling as improving temporal consistency and reducing compounding errors.Source: §2.3 · p. 2

2.4 Generalization in Multitask Learning

  • Multitask RL needs models that fit mixed-quality datasets and transfer to new tasks; diffusion models can model multimodal task data and adapt by estimating the task distribution.Existing alternatives may face conflicting gradients, low online sample efficiency, or high data and computational costs.Source: §2.4 · p. 2

3 Foundations of Diffusion Models

The survey introduces diffusion formulations and sampling techniques relevant to RL, then explains how diffusion models can serve different roles in the agent-environment-buffer cycle. It covers denoising and score-based models, conditional guidance, and methods for accelerating sampling.

3 Foundations of Diffusion Models

  • Diffusion models can act as planners, policies, or data synthesizers, differing from autoregressive planning, unimodal Gaussian policies, and random-perturbation augmentation.As planners they denoise whole trajectories; as policies they model arbitrary action distributions; as synthesizers they sample from a learned dataset distribution.Source: Figure 1 · p. 3

3.1 Denoising Diffusion Probabilistic Model

  • DDPM models data generation by reversing a multistep noising process, using learned Gaussian transitions to denoise pure noise into real data.A parameterized network predicts noise, and the simplified training loss is a weighted version of the evidence lower bound.Source: §3.1 · p. 3

3.2 Score-based Generative Models

  • Score-based models extend diffusion to continuous time, describing the forward and reverse processes with stochastic differential equations and estimating the score function with a parameterized model.The score is defined as the gradient of the log probability density of the noisy data.Source: §3.2 · p. 3

3.3 Guided Sampling Methods

  • Guided sampling targets p(x|y), conditioning generation on a desired attribute through classifier-based or classifier-free guidance.Classifier guidance uses a differentiable classifier on noisy samples; classifier-free guidance uses conditional and unconditional noise models.Source: §3.3 · pp. 3–4

3.4 Fast Sampling Methods

  • Fast sampling methods either avoid extra learning or train additional components to reduce diffusion's iterative sampling time.DDIM and DPM-solver are learning-free examples; TDPM and time-step selection are learning-based approaches, with the latter trading some sample quality for efficiency.Source: §3.4 · p. 4

4 The Roles of Diffusion Models in RL

Diffusion models are used as planners, policies, and data synthesizers in RL, with additional work applying them to value estimation, representations, and dynamics models.

4.1 Planner

  • Diffusion planners generate multi-step sequences simultaneously to avoid compounding errors from autoregressive planning, especially with limited offline data.Guided sampling targets high-reward trajectories; fast sampling and replacement of initial steps with observed history support online deployment and consistency with the agent’s current state.Source: §4.1 · pp. 4–5

4.2 Policy

  • Diffusion policies model actions conditioned on the current state, capturing multimodal behavior that conventional distance-based regularization may fail to fit.Diffusion-QL adds weighted Q maximization to training; other approaches use energy guidance or Q/energy values to select among sampled actions.Source: §4.2 · p. 5

4.3 Data Synthesizer

  • Diffusion synthesizers learn the data distribution from real RL data and generate diverse, dynamically consistent synthetic data for policy learning.In online settings, newly collected real data updates the diffusion model, while the model and policy are updated alternately.Source: §4.3 · p. 5

4.4 Others

  • Other uses include estimating value functions from discounted state occupancy, learning latent representations, and generating synthetic trajectories with a diffusion dynamics model.Latent representations condition Q-functions and policies; the dynamics-model approach alternates state denoising with Langevin dynamics of policy actions.Source: §4.4 · pp. 5–6

5 Applications of Diffusion Models

The survey reviews diffusion-model applications in offline and online RL, imitation learning, trajectory generation, and data augmentation.

5.1 Offline RL

  • Offline RL applications use diffusion models for trajectory planning, expressive policies, value and representation learning, and multitask and multi-agent decision-making.Examples include safety-constrained trajectory generation, hierarchical long-horizon planning, task adaptation, and modeling coordination among agents.Source: §5.1 · p. 6

5.2 Online RL

  • Online RL applications include action relabeling for policy improvement, one-step consistency-model policies, and diffusion dynamics models for synthetic trajectories.The survey notes that online RL applications are less explored than offline settings and merit further investigation.Source: §5.2 · pp. 6, 8

5.3 Imitation Learning

  • In imitation learning, diffusion policies represent multimodal behavior and generate temporally consistent closed-loop robot action sequences.Other applications include diverse manipulation skills, goal-conditioned policies, and hierarchical skills guided by visual observations and language instructions.Source: §5.3 · p. 8

5.4 Trajectory Generation

  • Trajectory-generation methods produce diverse, fine-grained human motions and robot interaction sequences under varied conditioning contexts.Some methods generate future video frames from which control actions are extracted; UniSim learns a universal simulator from diverse datasets.Source: §5.4 · p. 8

5.5 Data Augmentation

  • Diffusion-based data augmentation generates diverse, accurate data, and reported analyses find higher diversity and accuracy than prior augmentation methods.SynthER and MTDiff-s generate complete transitions; multi-task training is reported to outperform single-task training on multi-task offline datasets.Source: §5.5 · p. 8

6 Summary and Future Prospects

The survey categorizes diffusion models by their roles in RL and relates them to challenges including restricted expressiveness, data scarcity, compounding error, and multitask generalization. It identifies four future directions: generative simulation, safety constraints, retrieval-enhanced generation, and skill composition.

6 Summary and Future Prospects

  • The survey groups methods by diffusion models’ roles as planners, policies, data synthesizers, and less common value-function or representation models.It compares these roles with traditional solutions in relation to longstanding RL challenges.Source: §6 · p. 8
  • Existing generative-simulation work uses diffusion models in agents and buffers, while broader opportunities include generating environment dynamics, rewards, or opponents.Gen2Sim generates diverse objects for robot-manipulation simulations.Source: §6 · pp. 8–9
  • Classifier-guided sampling could treat safety constraints as conditions, allowing new requirements to be incorporated while diffusion-model parameters remain unchanged.The survey presents this as promising for safety requirements introduced after training.Source: §6 · p. 9
  • Retrieving relevant states as inputs may improve diffusion-based decisions in rare training states, and an updated retrieval dataset may support new behaviors without retraining.Source: §6 · p. 9
  • Diffusion models’ multimodal behavior modeling and guidance may support composing sub-skills, with offline RL experiments suggesting knowledge sharing and potential zero-shot adaptation.The survey also identifies continuous RL through skill composition as a potential application.Source: §6 · p. 9

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