7 articles tagged with #function-approximation. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers introduce λ-target updates, a novel mechanism that geometrically averages periodic hard target updates in linear Q-learning to improve stability. This theoretical advancement bridges traditional periodic updates and continuous projected Q-value iteration, with potential applications in reinforcement learning optimization.
Researchers present a novel inverse reinforcement learning framework that handles multiple imperfect demonstrators with varying suboptimality levels, using a feasible-reward-set approach with linear constraints. The method includes theoretical guarantees for reward recovery and practical algorithms tested on grid-worlds and LLM fine-tuning, addressing a significant gap in real-world IRL applications.
Researchers propose behavior-aware auxiliary corrections for off-policy temporal-difference learning, introducing BA-TDC and BA-TDRC algorithms that replace standard covariance matrices with behavior Bellman matrices to improve stability in value-function approximation. The work provides theoretical convergence guarantees and demonstrates that behavior-aware geometry significantly benefits performance on certain tasks, though regularization remains necessary for robustness across diverse settings.
Researchers propose SincKANs, a neural network architecture combining Sinc interpolation with Kolmogorov-Arnold Networks to improve function approximation and solve partial differential equations. The approach demonstrates superior performance compared to existing methods, particularly for functions with singularities, offering potential advances in physics-informed machine learning.
Researchers introduce Geometric Kolmogorov-Arnold Networks (GeoKANs), an advancement in KAN-type neural networks that learn geometry-adapted coordinate systems rather than relying on fixed Euclidean inputs. By adapting a diagonal Riemannian metric during training, GeoKAN redistributes computational capacity toward regions of rapid variation, making it particularly effective for physics-informed learning and differential equation problems.
Researchers present the first theoretical framework for differentially private reinforcement learning with general function approximation, achieving regret bounds of Õ(K^3/5) that match linear-case performance. This breakthrough extends privacy guarantees beyond tabular and linear settings, combining batched policy updates with the exponential mechanism for improved privacy-utility tradeoffs in online RL systems.
Researchers propose R-GTD, a regularized gradient temporal-difference learning algorithm that maintains convergence guarantees even when the feature interaction matrix becomes singular—a practical limitation in existing GTD methods. The geometric analysis provides explicit error bounds and addresses a key stability challenge in off-policy reinforcement learning with function approximation.
