arXiv:2605.23089v1 Announce Type: new Abstract: Model-based reinforcement learning improves sample efficiency by learning a world model. However, existing latent world models such as DreamerV3 do not explicitly enforce local smoothness in their learned transition dynamics, leaving a useful inductive bias for transition dynamics learning unexploited. We propose GPLD, a gradient-penalized latent dynamics regularizer for DreamerV3 that applies a row-wise Jacobian penalty to the posterior latent distribution to encourage locally smooth transition learning. We show that this penalty can be interpre
The continuous pursuit of more efficient and robust model-based reinforcement learning algorithms is essential for advancing AI capabilities, particularly in sample efficiency.
Improving the sample efficiency of world models via smoother latent dynamics accelerates the development of advanced AI agents, making them more practical for real-world applications.
This research introduces a method to make AI models learn transition dynamics more smoothly, potentially leading to faster and more reliable model-based learning.
- · AI developers
- · Robotics
- · Autonomous systems
- · Inefficient model-based RL approaches
More robust and sample-efficient AI models will emerge, particularly in reinforcement learning.
This could accelerate the development of sophisticated AI agents capable of complex tasks with less training data.
Increased efficiency in AI agent development may lead to broader adoption of autonomous systems across various industries.
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Read at arXiv cs.LG