Unifying Model-Free Efficiency and Model-Based Representations via Latent Dynamics

Researchers introduce Unified Latent Dynamics (ULD), a reinforcement learning algorithm that combines the sample efficiency of model-free methods with the representational advantages of model-based approaches without requiring planning overhead. The method achieves competitive performance across 80 diverse environments including continuous control, visual tasks, and Atari games with minimal hyperparameter tuning.

ULD addresses a fundamental challenge in reinforcement learning: the trade-off between sample efficiency and computational overhead. Traditional model-free methods learn policies directly but require large amounts of data, while model-based approaches build environment models for planning but introduce computational complexity. This research proposes embedding state-action pairs into a latent space where the value function becomes approximately linear, theoretically bridging both paradigms without the planning costs.

The breakthrough lies in the mathematical framework: the authors prove that their embedding-based temporal-difference updates converge to the same fixed point as linear model-based value expansion under mild conditions. This theoretical grounding provides confidence in the approach beyond empirical results. The algorithm employs synchronized network updates, auxiliary losses for predictive dynamics, and reward normalization—practical engineering choices that enable stable learning under sparse reward conditions.

The empirical validation across 80 environments demonstrates genuine generalization. Testing on Gym locomotion, DeepMind Control Suite (both proprioceptive and pixel-based), and Atari games shows the method achieves parity or superiority to specialized baselines while maintaining a smaller parameter footprint. This cross-domain competence with single hyperparameter settings addresses a persistent pain point in deep RL: the need for extensive domain-specific tuning.

For the broader AI research community, ULD suggests that value-aligned representations may be sufficient for achieving both adaptability and sample efficiency, potentially reducing the complexity of RL system design. The work indicates a path toward more general, efficient learning agents that don't sacrifice performance for simplicity.

• →ULD unifies model-free efficiency with model-based representations through latent space embeddings without planning overhead
• →Theoretical analysis proves embedding-based updates converge to linear model-based value expansion under specified conditions
• →Algorithm achieves competitive performance on 80 environments with single hyperparameter configuration and reduced parameters
• →Value-aligned latent representations alone can deliver sample efficiency traditionally requiring full environment models
• →Method combines synchronized network updates, predictive dynamics auxiliary losses, and reward normalization for stable sparse-reward learning