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AI Research
arXiv CS.AI·May 25·5 min read
Dreaming Smoothly and Sample Efficiently with Gradient Penalized Latent Dynamics
TL;DR
Model-based reinforcement learning agents learn world models to reduce data requirements, but existing latent world models like DreamerV3 lack explicit smoothness constraints on learned dynamics. GPLD adds a gradient penalty regularizer that encourages locally smooth transitions by penalizing Jacobian variation in the latent space.
✦ Why It Matters
Engineers can apply GPLD to improve sample efficiency and convergence speed in continuous control reinforcement learning systems with minimal implementation overhead.
Key Takeaways
- Add Jacobian-based smoothness regularization to existing latent world models for continuous control tasks.
- Use Hutchinson stochastic probes to efficiently estimate gradient penalties without full Jacobian computation.
- Expect faster convergence and improved sample efficiency on locomotion and continuous control benchmarks.
- Implement as a plug-in regularizer compatible with DreamerV3 and similar latent dynamics models.
Full Summary
Model-based reinforcement learning improves sample efficiency by learning a world model—a learned representation of how the environment evolves—rather than learning directly from raw experience. DreamerV3, a state-of-the-art latent world model, learns compressed representations but does not enforce local smoothness in transition dynamics (the rules governing state changes).
This work introduces GPLD (Gradient-Penalized Latent Dynamics), a regularization technique that applies row-wise Jacobian penalties to encourage smooth transitions in the learned latent space. The penalty is interpreted as the continuous analog of finite-difference smoothing and is computed efficiently using Hutchinson-style stochastic probes—a variance-reduction technique.
Empirical evaluation on DeepMind Control Suite proprioceptive tasks (vision-free continuous control) demonstrated improved aggregate sample efficiency, with particularly strong gains on higher-complexity quadruped locomotion. GPLD agents reached high-return behavior earlier and maintained more consistent learning over extended horizons.