Diffusion for World Modeling: Visual Details Matter in Atari
- URL: http://arxiv.org/abs/2405.12399v1
- Date: Mon, 20 May 2024 22:51:05 GMT
- Title: Diffusion for World Modeling: Visual Details Matter in Atari
- Authors: Eloi Alonso, Adam Jelley, Vincent Micheli, Anssi Kanervisto, Amos Storkey, Tim Pearce, François Fleuret,
- Abstract summary: We introduce DIAMOND (DIffusion As a Model Of eNvironment Dreams), a reinforcement learning agent trained in a diffusion world model.
We analyze the key design choices that are required to make diffusion suitable for world modeling, and demonstrate how improved visual details can lead to improved agent performance.
- Score: 22.915802013352465
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: World models constitute a promising approach for training reinforcement learning agents in a safe and sample-efficient manner. Recent world models predominantly operate on sequences of discrete latent variables to model environment dynamics. However, this compression into a compact discrete representation may ignore visual details that are important for reinforcement learning. Concurrently, diffusion models have become a dominant approach for image generation, challenging well-established methods modeling discrete latents. Motivated by this paradigm shift, we introduce DIAMOND (DIffusion As a Model Of eNvironment Dreams), a reinforcement learning agent trained in a diffusion world model. We analyze the key design choices that are required to make diffusion suitable for world modeling, and demonstrate how improved visual details can lead to improved agent performance. DIAMOND achieves a mean human normalized score of 1.46 on the competitive Atari 100k benchmark; a new best for agents trained entirely within a world model. To foster future research on diffusion for world modeling, we release our code, agents and playable world models at https://github.com/eloialonso/diamond.
Related papers
- Diffusion Models and Representation Learning: A Survey [3.8861148837000856]
This survey explores the interplay between diffusion models and representation learning.
It provides an overview of diffusion models' essential aspects, including mathematical foundations.
Various approaches related to diffusion models and representation learning are detailed.
arXiv Detail & Related papers (2024-06-30T17:59:58Z) - Simplified and Generalized Masked Diffusion for Discrete Data [47.711583631408715]
Masked (or absorbing) diffusion is actively explored as an alternative to autoregressive models for generative modeling of discrete data.
In this work, we aim to provide a simple and general framework that unlocks the full potential of masked diffusion models.
arXiv Detail & Related papers (2024-06-06T17:59:10Z) - DEEM: Diffusion Models Serve as the Eyes of Large Language Models for Image Perception [66.88792390480343]
We propose DEEM, a simple and effective approach that utilizes the generative feedback of diffusion models to align the semantic distributions of the image encoder.
DEEM exhibits enhanced robustness and a superior capacity to alleviate hallucinations while utilizing fewer trainable parameters, less pre-training data, and a smaller base model size.
arXiv Detail & Related papers (2024-05-24T05:46:04Z) - An Overview of Diffusion Models: Applications, Guided Generation, Statistical Rates and Optimization [59.63880337156392]
Diffusion models have achieved tremendous success in computer vision, audio, reinforcement learning, and computational biology.
Despite the significant empirical success, theory of diffusion models is very limited.
This paper provides a well-rounded theoretical exposure for stimulating forward-looking theories and methods of diffusion models.
arXiv Detail & Related papers (2024-04-11T14:07:25Z) - Self-Play Fine-Tuning of Diffusion Models for Text-to-Image Generation [59.184980778643464]
Fine-tuning Diffusion Models remains an underexplored frontier in generative artificial intelligence (GenAI)
In this paper, we introduce an innovative technique called self-play fine-tuning for diffusion models (SPIN-Diffusion)
Our approach offers an alternative to conventional supervised fine-tuning and RL strategies, significantly improving both model performance and alignment.
arXiv Detail & Related papers (2024-02-15T18:59:18Z) - SODA: Bottleneck Diffusion Models for Representation Learning [75.7331354734152]
We introduce SODA, a self-supervised diffusion model, designed for representation learning.
The model incorporates an image encoder, which distills a source view into a compact representation, that guides the generation of related novel views.
We show that by imposing a tight bottleneck between the encoder and a denoising decoder, we can turn diffusion models into strong representation learners.
arXiv Detail & Related papers (2023-11-29T18:53:34Z) - Diff-Instruct: A Universal Approach for Transferring Knowledge From
Pre-trained Diffusion Models [77.83923746319498]
We propose a framework called Diff-Instruct to instruct the training of arbitrary generative models.
We show that Diff-Instruct results in state-of-the-art single-step diffusion-based models.
Experiments on refining GAN models show that the Diff-Instruct can consistently improve the pre-trained generators of GAN models.
arXiv Detail & Related papers (2023-05-29T04:22:57Z) - A Survey on Generative Diffusion Model [75.93774014861978]
Diffusion models are an emerging class of deep generative models.
They have certain limitations, including a time-consuming iterative generation process and confinement to high-dimensional Euclidean space.
This survey presents a plethora of advanced techniques aimed at enhancing diffusion models.
arXiv Detail & Related papers (2022-09-06T16:56:21Z)
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.