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[2602.12643] Unifying Model-Free Efficiency and Model-Based Representations via Latent Dynamics
Summary
The paper presents Unified Latent Dynamics (ULD), a novel reinforcement learning algorithm that combines the efficiency of model-free methods with the strengths of model-based approaches, achieving high performance across various environments.
Why It Matters
This research addresses a significant challenge in reinforcement learning by merging model-free and model-based techniques, potentially enhancing adaptability and efficiency in AI applications. The findings could lead to more robust AI systems capable of performing well in diverse scenarios with minimal tuning.
Key Takeaways
- Unified Latent Dynamics (ULD) combines model-free efficiency with model-based representation strengths.
- The algorithm supports a single set of hyperparameters across various domains, simplifying implementation.
- ULD achieves competitive performance in 80 environments, including Atari and DeepMind Control tasks.
- The method employs synchronized updates and auxiliary losses for stable learning under sparse rewards.
- Value-aligned latent representations can enhance adaptability and sample efficiency without full model-based planning.
Computer Science > Machine Learning arXiv:2602.12643 (cs) [Submitted on 13 Feb 2026] Title:Unifying Model-Free Efficiency and Model-Based Representations via Latent Dynamics Authors:Jashaswimalya Acharjee, Balaraman Ravindran View a PDF of the paper titled Unifying Model-Free Efficiency and Model-Based Representations via Latent Dynamics, by Jashaswimalya Acharjee and Balaraman Ravindran View PDF 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 (pro...
