Diffusion for World Modeling: Visual Details Matter in Atari

id:: 2405.12399
Authors:: Eloi Alonso, Adam Jelley, Vincent Micheli, Anssi Kanervisto, Amos Storkey, Tim Pearce, François Fleuret
Published:: 2024-05-20
arXiv:: https://arxiv.org/abs/2405.12399
PDF:: https://arxiv.org/pdf/2405.12399
DOI:: N/A
Journal Reference:: N/A
Primary Category:: cs.LG
Categories:: cs.LG, cs.AI, cs.CV
Comment:: NeurIPS 2024 (Spotlight)
github_url:: _

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. We further demonstrate that DIAMOND’s diffusion world model can stand alone as an interactive neural game engine by training on static Counter-Strike: Global Offensive gameplay. To foster future research on diffusion for world modeling, we release our code, agents, videos and playable world models at https://diamond-wm.github.io.

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1. Brief Overview

This paper introduces DIAMOND (DIffusion As a Model Of eNvironment Dreams), a reinforcement learning agent trained within a diffusion world model. Unlike previous world models that primarily utilize discrete latent variables, DIAMOND leverages diffusion models for image generation, enabling the preservation of crucial visual details. This approach leads to significant performance improvements, achieving a new state-of-the-art on the Atari 100k benchmark. The authors also demonstrate the standalone capabilities of DIAMOND’s diffusion world model as an interactive neural game engine by training it on Counter-Strike: Global Offensive gameplay. The code and models are publicly available.

2. Key Points

DIAMOND utilizes diffusion models for world modeling, retaining important visual details often lost in discrete latent representations.
Achieves a mean human normalized score of 1.46 on the Atari 100k benchmark, surpassing previous world model agents.
Functions as a standalone interactive neural game engine, demonstrated through training on Counter-Strike: Global Offensive gameplay.
EDM (Energy-based Diffusion Model) is chosen over DDPM (Denoising Diffusion Probabilistic Model) due to its superior stability over long time horizons.
Code, agents, videos, and playable world models are publicly released.

3. Notable Quotes

None explicitly stated.

4. Primary Themes

World Modeling with Diffusion Models: The core theme focuses on the novel application of diffusion models to create more realistic and informative world models for reinforcement learning. The authors highlight the advantages of this approach over traditional methods using discrete latent spaces.
Sample Efficiency in Reinforcement Learning: The improved performance of DIAMOND on the Atari 100k benchmark underscores the potential of diffusion-based world models to enhance sample efficiency in RL.
Generative Game Engines: The successful application of the model to Counter-Strike: Global Offensive showcases the potential of diffusion models for creating interactive, playable neural game environments.
Visual Details in RL: The paper emphasizes the critical role of visual detail in reinforcement learning and demonstrates how preserving these details leads to significantly improved agent performance.