Continuous Control - Edgentiq

Geometric Action Control: A Simpler Path to Continuous Reinforcement Learning

Tiny Bits, Big Impact: Quantized AI Controllers for Real-Time Robotics

Q3C: Stable Continuous Control Without an Actor

Unlocking Transformer Potential in Online Continuous Control

Ensuring Reliable AI: A New Approach to Generalizable Reinforcement Learning

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Kontinuous Kontext: Unlocking Fine-Grained Control in AI Image Editing

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Policy Optimization-Model Predictive Control: A Unified Approach to Learning and Planning in Reinforcement Learning

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Precise Image Editing: Introducing SAEdit’s Token-Level Control

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Efficient Imitation Learning in Low-Data Scenarios: The Noise-Guided Transport Method

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Frictional Q-learning: A Physics-Inspired Approach to Stable Reinforcement Learning

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Advanced Spiking Neural Networks Achieve Precise Robotic Control

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AI Agents Learn Diverse Behaviors with New Categorical Policy Approach

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CAMAR: Bridging the Gap in Continuous Multi-Agent Reinforcement Learning

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Advancing Robotic Control with Continuous Group Relative Policy Optimization

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Geometric Insights into Neural Reinforcement Learning

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Offline Planning for Complex Decision-Making: Introducing Partially Observable Monte-Carlo Graph Search

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Gen AI News and Updates

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Geometric Action Control: A Simpler Path to Continuous Reinforcement Learning

November 12, 2025

Tiny Bits, Big Impact: Quantized AI Controllers for Real-Time Robotics

November 11, 2025

Q3C: Stable Continuous Control Without an Actor

October 22, 2025