An LLM-based self-evolving agent discovers a traveling-wave controller with body-frame guidance and yaw feedback that generalizes to unseen targets for an underactuated fluid swimmer.
A survey on physics informed reinforcement learning: Review and open problems
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TRIDENT is a MARL framework using Richardson-Romberg gradient correction, Lyapunov-constrained trust-region updates, and a physics-informed residual critic that claims O(1/sqrt(K)) convergence to constrained Nash equilibrium with O(sqrt(K)) violation bounds and large reductions in training violation
SALSA-RL introduces latent-space stability analysis for actions of pretrained RL agents using encoder-decoder and state-dependent linear dynamics to enable non-invasive interpretability.
citing papers explorer
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Self-Evolving Scientific Agent Discovers Generalizable Physically-Reasoned Fluid Control
An LLM-based self-evolving agent discovers a traveling-wave controller with body-frame guidance and yaw feedback that generalizes to unseen targets for an underactuated fluid swimmer.
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TRIDENT: Breaking the Hybrid-Safety-Physics Coupling for Provably Safe Multi-Agent Reinforcement Learning
TRIDENT is a MARL framework using Richardson-Romberg gradient correction, Lyapunov-constrained trust-region updates, and a physics-informed residual critic that claims O(1/sqrt(K)) convergence to constrained Nash equilibrium with O(sqrt(K)) violation bounds and large reductions in training violation
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SALSA-RL: Stability Analysis in the Latent Space of Actions for Reinforcement Learning
SALSA-RL introduces latent-space stability analysis for actions of pretrained RL agents using encoder-decoder and state-dependent linear dynamics to enable non-invasive interpretability.