Path-Space Mirror Descent for On-Policy Reinforcement Learning under the Generalized Schr\"odinger Bridge
arXiv:2603.21621v2 Announce Type: replace Abstract: Classical on-policy algorithms such as PPO and mirror descent policy optimization provide stable proximal policy updates through tractable action likelihoods, but are typically instantiated with simple Gaussian policies whose expressiveness can be limited in complex continuous-control tasks. Generative policies based on diffusion and flow models provide more expressive action distributions, but they naturally define distributions over multi-step denoising paths whose terminal action density is often intractable, creating a mismatch with likel
The paper represents an advancement in addressing the limitations of classical on-policy reinforcement learning algorithms when applied to complex continuous-control tasks, spurred by the growing need for more expressive policy models.
This research provides a theoretical framework for integrating highly expressive generative models like diffusion and flow models into reinforcement learning, which could significantly improve the performance and applicability of AI in complex physical systems.
The development of 'Path-Space Mirror Descent' offers a new optimization method for on-policy reinforcement learning that can leverage the power of generative policies, potentially enabling more sophisticated AI behaviors than previously possible.
- · AI researchers
- · Robotics companies
- · Advanced manufacturing
- · AI software developers
- · Developers reliant on limited Gaussian policies
- · Classical control systems
Improved performance of AI systems in complex, real-world continuous control environments.
Accelerated development of autonomous agents and robots capable of handling intricate tasks with greater flexibility and precision.
Increased automation and efficiency in industries requiring fine-grained control, potentially leading to new product categories and economic models.
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