#value-function News & Analysis

4 articles tagged with #value-function. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

4 articles

AINeutralarXiv – CS AI · Jun 236/10

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Sim2O: Efficient Offline-to-Online MARL via Joint Action Composition

Researchers introduce Sim2O, a new framework for offline-to-online multi-agent reinforcement learning (MARL) that combines offline and online action proposals through dynamic blending rather than monolithic joint decisions. The minimalist approach leverages centralized value functions to identify high-value coordination strategies without auxiliary training, demonstrating significant performance improvements over existing baselines.

AINeutralarXiv – CS AI · Jun 196/10

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Stabilizing the Q-Gradient Field for Policy Smoothness in Actor-Critic Methods

Researchers present PAVE, a theoretical and practical framework addressing policy instability in actor-critic reinforcement learning by stabilizing the critic's Q-function gradient field rather than directly regularizing policy outputs. The work demonstrates that policy smoothness is fundamentally determined by the critic's differential geometry, offering a more principled approach to deploying learned policies in physical systems.

AINeutralarXiv – CS AI · Jun 46/10

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Unifying Model-Free Efficiency and Model-Based Representations via Latent Dynamics

Researchers introduce Unified Latent Dynamics (ULD), a reinforcement learning algorithm that combines the sample efficiency of model-free methods with the representational advantages of model-based approaches without requiring planning overhead. The method achieves competitive performance across 80 diverse environments including continuous control, visual tasks, and Atari games with minimal hyperparameter tuning.

🏢 Google

AINeutralarXiv – CS AI · May 296/10

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Behavior-Aware Auxiliary Corrections for Off-Policy Temporal-Difference Prediction

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.