Unifying Model-Free Efficiency and Model-Based Representations via Latent Dynamics
arXiv:2602.12643v1 Announce Type: new Abstract: We present Unified Latent Dynamics (ULD), a novel reinforcement learning algorithm that unifies the efficiency of model-free methods with the representational strengths of model-based approaches, without incurring planning overhead. By embedding state-action pairs into a latent space in which the true value function is approximately linear, our method supports a single set of hyperparameters across diverse domains -- from continuous control with low-dimensional and pixel inputs to high-dimensional Atari games. We prove that, under mild conditions, the fixed point of our embedding-based temporal-difference updates coincides with that of a corresponding linear model-based value expansion, and we derive explicit error bounds relating embedding fidelity to value approximation quality. In practice, ULD employs synchronized updates of encoder, value, and policy networks, auxiliary losses for short-horizon predictive dynamics, and reward-scale
arXiv:2602.12643v1 Announce Type: new Abstract: We present Unified Latent Dynamics (ULD), a novel reinforcement learning algorithm that unifies the efficiency of model-free methods with the representational strengths of model-based approaches, without incurring planning overhead. By embedding state-action pairs into a latent space in which the true value function is approximately linear, our method supports a single set of hyperparameters across diverse domains -- from continuous control with low-dimensional and pixel inputs to high-dimensional Atari games. We prove that, under mild conditions, the fixed point of our embedding-based temporal-difference updates coincides with that of a corresponding linear model-based value expansion, and we derive explicit error bounds relating embedding fidelity to value approximation quality. In practice, ULD employs synchronized updates of encoder, value, and policy networks, auxiliary losses for short-horizon predictive dynamics, and reward-scale normalization to ensure stable learning under sparse rewards. Evaluated on 80 environments spanning Gym locomotion, DeepMind Control (proprioceptive and visual), and Atari, our approach matches or exceeds the performance of specialized model-free and general model-based baselines -- achieving cross-domain competence with minimal tuning and a fraction of the parameter footprint. These results indicate that value-aligned latent representations alone can deliver the adaptability and sample efficiency traditionally attributed to full model-based planning.
Executive Summary
The article presents Unified Latent Dynamics (ULD), a novel reinforcement learning algorithm that bridges the gap between model-free and model-based approaches. ULD embeds state-action pairs into a latent space where the true value function is approximately linear, enabling efficient learning without planning overhead. The method is evaluated across diverse domains, including continuous control and high-dimensional Atari games, and demonstrates superior performance with minimal tuning and fewer parameters. Theoretical guarantees and practical implementations are provided, showcasing ULD's adaptability and sample efficiency.
Key Points
- ▸ ULD unifies model-free efficiency with model-based representational strengths.
- ▸ The algorithm embeds state-action pairs into a latent space for linear value function approximation.
- ▸ ULD achieves cross-domain competence with minimal tuning and fewer parameters.
- ▸ Theoretical error bounds relate embedding fidelity to value approximation quality.
- ▸ Evaluated on 80 environments, ULD matches or exceeds specialized baselines.
Merits
Theoretical Rigor
The article provides theoretical guarantees, including fixed point analysis and error bounds, which strengthen the credibility of the proposed method.
Practical Performance
ULD demonstrates superior performance across diverse environments, indicating its adaptability and efficiency.
Minimal Tuning
The method requires minimal hyperparameter tuning, making it practical for real-world applications.
Demerits
Complexity of Implementation
The synchronized updates of encoder, value, and policy networks, along with auxiliary losses, may complicate implementation.
Assumptions and Conditions
The theoretical guarantees rely on mild conditions that may not hold in all practical scenarios.
Limited Real-World Testing
While evaluated on various simulated environments, real-world applications and robustness to noise or adversarial inputs are not thoroughly explored.
Expert Commentary
The article presents a significant advancement in the field of reinforcement learning by unifying model-free and model-based approaches through latent space representations. The theoretical underpinnings, including the fixed point analysis and error bounds, provide a robust foundation for the proposed method. The practical evaluations across diverse environments demonstrate the method's adaptability and efficiency, making it a strong candidate for real-world applications. However, the complexity of implementation and the reliance on mild conditions for theoretical guarantees warrant further investigation. The method's minimal tuning requirements and superior performance can significantly impact both practical applications and policy decisions, particularly in domains requiring efficient and adaptable reinforcement learning solutions.
Recommendations
- ✓ Further research should explore the robustness of ULD in real-world scenarios with noise and adversarial inputs.
- ✓ Investigating the scalability of ULD to even more complex and high-dimensional environments would be beneficial.
- ✓ Policy makers should consider the implications of ULD's efficiency and adaptability in the adoption of reinforcement learning in critical applications.