Reinforcement learning limits its application due to low sample efficiency, but world models as environment generation models bring hope to solve this problem. It can efficiently train reinforcement learning agents, however most world models use discrete latent variable sequences to simulate environmental dynamics, which may ignore critical visual details. The editor of Downcodes brings you an interpretation of DIAMOND (Ambient Dream Diffusion Model), which uses the diffusion model to train reinforcement learning agents and achieved excellent results in the Atari 100k benchmark test.
Currently, most world models simulate environmental dynamics through discrete latent variable sequences. However, this method of compressing into a compact discrete representation may ignore visual details that are crucial for reinforcement learning.
At the same time, diffusion models have become the dominant method in the field of image generation, challenging traditional discrete latent variable modeling methods. Inspired by this, the researchers proposed a new method called DIAMOND (ambient dream diffusion model), which is a reinforcement learning agent trained in a diffusion world model. DIAMOND has made key design choices to ensure the efficiency and stability of the diffusion model over long periods of time.
DIAMOND achieved an average human-normalized score of 1.46 on the famous Atari100k benchmark, the best result for an agent trained entirely on a model of the world. Furthermore, the advantage of operating in image space is that the diffuse world model can be a direct substitute for the environment, allowing for a better understanding of the world model and the behavior of the agent. Researchers found that performance improvements in some games stem from better modeling of key visual details.
The success of DIAMOND is due to the choice of EDM (Elucidating the Design Space of Diffusion-based Generative Models) framework. Compared with traditional DDPM (Denoising Diffusion Probabilistic Models), EDM exhibits higher stability with fewer denoising steps, avoiding serious cumulative errors in the model over a long period of time.
Additionally, DIAMOND demonstrated the ability of its diffuse world model to serve as an interactive neural game engine. By training on 87 hours of static Counter-Strike: Global Offensive game data, DIAMOND successfully generated an interactive Dust II map neural game engine.
In the future, DIAMOND can further improve its performance by integrating more advanced memory mechanisms, such as autoregressive Transformers. In addition, integrating reward/termination predictions into diffusion models is also a direction worth exploring.
Paper address: https://arxiv.org/pdf/2405.12399
The emergence of DIAMOND has brought new breakthroughs in the field of reinforcement learning. Its excellent performance in Atari games and "Counter-Strike" games demonstrates the great potential of the diffusion model in building efficient world models. In the future, with the further development of technology, DIAMOND and its derivative technologies are expected to be applied in more fields and promote the advancement of artificial intelligence technology. Looking forward to more research results on reinforcement learning based on diffusion models.