World Models via Policy-Guided Trajectory Diffusion (2312.08533v4)

Abstract: World models are a powerful tool for developing intelligent agents. By predicting the outcome of a sequence of actions, world models enable policies to be optimised via on-policy reinforcement learning (RL) using synthetic data, i.e. in "in imagination". Existing world models are autoregressive in that they interleave predicting the next state with sampling the next action from the policy. Prediction error inevitably compounds as the trajectory length grows. In this work, we propose a novel world modelling approach that is not autoregressive and generates entire on-policy trajectories in a single pass through a diffusion model. Our approach, Policy-Guided Trajectory Diffusion (PolyGRAD), leverages a denoising model in addition to the gradient of the action distribution of the policy to diffuse a trajectory of initially random states and actions into an on-policy synthetic trajectory. We analyse the connections between PolyGRAD, score-based generative models, and classifier-guided diffusion models. Our results demonstrate that PolyGRAD outperforms state-of-the-art baselines in terms of trajectory prediction error for short trajectories, with the exception of autoregressive diffusion. For short trajectories, PolyGRAD obtains similar errors to autoregressive diffusion, but with lower computational requirements. For long trajectories, PolyGRAD obtains comparable performance to baselines. Our experiments demonstrate that PolyGRAD enables performant policies to be trained via on-policy RL in imagination for MuJoCo continuous control domains. Thus, PolyGRAD introduces a new paradigm for accurate on-policy world modelling without autoregressive sampling.

• The paper presents the innovative PolyGRAD method, replacing autoregressive sampling with diffusion-based trajectory generation to significantly reduce cumulative prediction errors.
• It employs a denoising model with dynamic policy guidance to create entire on-policy trajectories in a single computational pass.
• Extensive experiments, particularly on MuJoCo environments, validate PolyGRAD's competitive performance and its potential to enhance scalable reinforcement learning applications.

Analyzing "World Models via Policy-Guided Trajectory Diffusion"

The paper "World Models via Policy-Guided Trajectory Diffusion" introduces an innovative approach to world modeling in reinforcement learning (RL) that challenges the traditional autoregressive paradigm. The authors propose a method called Policy-Guided Trajectory Diffusion (PolyGRAD) which leverages diffusion models to generate on-policy trajectories in a single computational pass, thereby circumventing the error accumulation typically associated with autoregressive models.

Overview

PolyGRAD addresses a critical shortcoming of existing world models used in reinforcement learning, which is their reliance on autoregressive processes that interleave state prediction with policy-based action sampling. In such frameworks, prediction errors compound as the trajectory length increases, undermining the efficacy of synthetic data generated for policy optimization. PolyGRAD eschews autoregressive sampling, instead using a denoising model in tandem with the policy to diffuse random trajectories into coherent on-policy sequences.

The PolyGRAD Approach

The core innovation in PolyGRAD lies in applying diffusion models to RL world modeling, allowing for the creation of entire on-policy trajectories without sequential sampling. The methodology involves:

1. Denoising Model Training: The model learns to predict the noise added to state and reward sequences, conditioned on actions. This differs from standard autoregressive approaches by considering entire trajectories rather than one-step transitions.
2. Policy Guidance: Instead of extracting actions directly from a neural policy model, PolyGRAD guides trajectory diffusion via the gradient of the policy's action distribution. This allows the generation of trajectories that are congruent with the policy's statistical characteristics without requiring iterative state-by-state prediction.
3. Automatic Tuning: The magnitude of action updates is dynamically adjusted to ensure that the synthetic trajectories preserve the distributional properties of the policy, maintaining on-policy characteristics throughout training.

Through extensive experimentation, the authors demonstrate that PolyGRAD can outperform traditional models in short trajectory generation while matching the performance of state-of-the-art models in longer trajectories. This suggests that PolyGRAD offers a computationally efficient alternative, requiring fewer resources than models that maintain arcs of autoregressive sampling, particularly in environments like MuJoCo.

Implications and Future Directions

The implications of this work span both theoretical and practical domains. On the theoretical side, the application of diffusion models in RL provides a framework that bypasses the iterative error accumulation inherent in traditional methods, potentially leading to more robust policy optimization. Practically, the reduced computational demand of PolyGRAD could enhance the scalability of RL applications where resources are constrained.

Nevertheless, PolyGRAD's efficacy in handling low-entropy policy distributions suggests room for further refinement. Future work could enhance the model's adaptability across various policy entropy levels or extend its application to more complex, high-dimensional landscapes beyond the MuJoCo suite tested.

Overall, "World Models via Policy-Guided Trajectory Diffusion" presents a significant step forward in the evolution of model-based reinforcement learning, establishing a foundation for future advancements in using non-autoregressive sampling methods in synthetic trajectory generation.