The world model also spreads! The trained agent turns out to be pretty good

World models provide a way to train reinforcement learning agents in a safe and sample-efficient manner. Recently, world models have mainly operated on discrete latent variable sequences to simulate environmental dynamics.

However, this method of compressing into compact discrete representations may ignore visual details that are important for reinforcement learning. On the other hand, diffusion models have become the dominant method for image generation, posing challenges to discrete latent models.

To promote this paradigm shift, researchers from the University of Geneva, the University of Edinburgh, and Microsoft Research jointly proposed a reinforcement learning agent trained in a diffuse world model—DIAMOND (DIffusion As a Model Of eNvironment Dreams).

• ##Paper address: https://arxiv .org/abs/2405.12399
• Project address: https://github.com/eloialonso/diamond
• Paper title: Diffusion for World Modeling: Visual Details Matter in Atari

In the Atari 100k benchmark, DIAMOND+ achieved an average Human Normalized Score (HNS) of 1.46. This means that an agent trained in the world model can be fully trained at the SOTA level of an agent trained in the world model. This study provides a stability analysis to illustrate that DIAMOND design choices are necessary to ensure the long-term efficient stability of the diffusive world model.

In addition to the benefit of operating in image space, it enables the diffuse world model to become a direct representation of the environment, thus providing a deeper understanding of the world model and agent behavior. In particular, the study found that performance improvements in certain games result from better modeling of key visual details.

Method Introduction

Next, this article introduces DIAMOND, a reinforcement learning agent trained in a diffusion world model. Specifically, we base this on the drift and diffusion coefficients f and g introduced in Section 2.2, which correspond to a specific choice of diffusion paradigm. Furthermore, this study also chose the EDM formulation based on Karras et al.

First define a disturbance kernel, , where is a real-valued function related to the diffusion time, called the noise schedule. This corresponds to setting the drift and diffusion coefficients to and .

Then use the network preprocessing introduced by Karras et al. (2022), and parameterize in formula (5) as noise observations and neural network predictions Weighted sum of values:

Obtain formula (6)

For the sake of concise definition,

includes all condition variables.

Preprocessor selection. Choose preprocessors and to maintain unit variance of network inputs and outputs at any noise level . is the empirical conversion of noise level, is given by and the standard deviation of the data distribution , the formula is

Combined with formula 5 and 6. Get the training target:

##This study uses standard U-Net 2D to construct the vector field, and retain a buffer containing the past L observations and actions to condition the model. Next they concatenated these past observations channel-wise with the next noisy observation and fed the actions into the residual block of U-Net via an adaptive group normalization layer. As discussed in Section 2.3 and Appendix A, there are many possible sampling methods to generate the next observation from a trained diffusion model. While the code base released by the study supports multiple sampling schemes, the study found that Euler methods are effective without requiring additional NFE (number of function evaluations) and avoiding the unnecessary complexity of higher-order samplers or random sampling. Effective.

Experiment

To fully evaluate DIAMOND, the study used the well-established Atari 100k benchmark, which included 26 games, using For testing the broad capabilities of an agent. For each game, the agent was only allowed 100k actions in the environment, which is roughly equivalent to 2 hours of human game time, to learn to play the game before being evaluated. For reference, an Atari agent without constraints is typically trained for 50 million steps, which corresponds to a 500-fold increase in experience. The researchers trained DIAMOND from scratch on each game using 5 random seeds. Each run used approximately 12GB of VRAM and took approximately 2.9 days on a single Nvidia RTX 4090 (1.03 GPU years total).

Table 1 compares different scores for training agents on the world model:

##The mean and IQM (Interquartile Mean) confidence intervals are provided in Figure 2:

The results show that, DIAMOND performed strongly on benchmarks, outperforming human players in 11 games and achieving an HNS score of 1.46, a new record for an agent trained entirely on a world model. The study also found that DIAMOND performs particularly well in environments where detail needs to be captured, such as Asterix, Breakout and Road Runner.

In order to study the stability of diffusion variables, this study analyzed the imagined trajectory generated by autoregression, as shown in Figure 3 below:

The study found that some situations require an iterative solver to drive the sampling process to a specific mode, such as the boxing game shown in Figure 4:

As shown in Figure 5, compared with the trajectories imagined by IRIS, the trajectories imagined by DIAMOND generally have higher visual quality and are more consistent with the real environment.

Interested readers can read the original text of the paper to learn more about the research content.

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