#markov-decision-processes News & Analysis

4 articles tagged with #markov-decision-processes. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

4 articles

AINeutralarXiv – CS AI · 3d ago6/10

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Diffusion-Augmented Markov Decision Processes for Maximum Entropy Reinforcement Learning

Researchers have developed Diffusion-Augmented Markov Decision Processes (DA-MDPs), a framework that integrates diffusion models into maximum entropy reinforcement learning to sample from optimal policy trajectory distributions. The approach is tested on three RL algorithms (PPO, WPO, REPPO) and demonstrates competitive or superior performance on continuous-control tasks while excelling at modeling multimodal action distributions.

AINeutralarXiv – CS AI · May 126/10

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Attribution-based Explanations for Markov Decision Processes

Researchers have developed attribution techniques that explain decision-making in Markov Decision Processes (MDPs), extending explainability methods beyond static inputs to sequential decision-making systems. The approach assigns importance scores to states and execution paths, enabling more interpretable AI agents in dynamic environments.

AINeutralarXiv – CS AI · Mar 44/105

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Robust Counterfactual Inference in Markov Decision Processes

Researchers propose a novel non-parametric method for robust counterfactual inference in Markov Decision Processes that computes tight bounds across all compatible causal models. The approach provides closed-form expressions instead of requiring exponentially complex optimization problems, making it highly efficient and scalable for real-world applications.

AINeutralarXiv – CS AI · Mar 24/106

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Resilient Strategies for Stochastic Systems: How Much Does It Take to Break a Winning Strategy?

Researchers introduce resilient strategies for stochastic systems, focusing on decision-making that remains robust against disturbances that could flip agent decisions. The work presents fundamental problems for Markov decision processes with reachability and safety objectives, extending to stochastic games with various disturbance aggregation methods.