Papers Continuous Control
“Continuous Control” 태그가 달린 논문 1,432편 · 필터 해제
SEBA: Sample-Efficient Black-Box Attacks on Visual Reinforcement Learning
Visual reinforcement learning has achieved remarkable progress in visual control and robotics, but its vulnerability to adversarial perturbations remains underexplored. Most existing black-box attacks focus on vector-bas…
Reinforcement LearningContinuous ControlViPRA: Video Prediction for Robot Actions
Can we turn a video prediction model into a robot policy? Videos, including those of humans or teleoperated robots, capture rich physical interactions. However, most of them lack labeled actions, which limits their use i…
Continuous ControlVideo PredictionTest-driven Reinforcement Learning in Continuous Control
Reinforcement learning (RL) has been recognized as a powerful tool for robot control tasks. RL typically employs reward functions to define task objectives and guide agent learning. However, since the reward function ser…
Reinforcement LearningContinuous ControlBeyond Distributions: Geometric Action Control for Continuous Reinforcement Learning
Gaussian policies have dominated continuous control in deep reinforcement learning (RL), yet they suffer from a fundamental mismatch: their unbounded support requires ad-hoc squashing functions that distort the geometry …
Reinforcement LearningContinuous ControlDynamics-Decoupled Trajectory Alignment for Sim-to-Real Transfer in Reinforcement Learning for Autonomous Driving
Reinforcement learning (RL) has shown promise in robotics, but deploying RL on real vehicles remains challenging due to the complexity of vehicle dynamics and the mismatch between simulation and reality. Factors such as …
Reinforcement LearningContinuous ControlAutonomous DrivingMotion PlanningQuantum Boltzmann Machines for Sample-Efficient Reinforcement Learning
We introduce theoretically grounded Continuous Semi-Quantum Boltzmann Machines (CSQBMs) that supports continuous-action reinforcement learning. By combining exponential-family priors over visible units with quantum Boltz…
Reinforcement LearningContinuous ControlLearning Without Critics? Revisiting GRPO in Classical Reinforcement Learning Environments
Group Relative Policy Optimization (GRPO) has emerged as a scalable alternative to Proximal Policy Optimization (PPO) by eliminating the learned critic and instead estimating advantages through group-relative comparisons…
Reinforcement LearningContinuous ControlAILA--First Experiments with Localist Language Models
This paper presents the first empirical demonstration of controllable locality in transformer language models, a novel architectural framework that enables continuous control over the degree of representation localizatio…
Continuous ControliFlyBot-VLA Technical Report
We introduce iFlyBot-VLA, a large-scale Vision-Language-Action (VLA) model trained under a novel framework. The main contributions are listed as follows: (1) a latent action model thoroughly trained on large-scale human …
Continuous ControlAn Integrated Approach to Neural Architecture Search for Deep Q-Networks
The performance of deep reinforcement learning agents is fundamentally constrained by their neural network architecture, a choice traditionally made through expensive hyperparameter searches and then fixed throughout tra…
Neural Architecture SearchReinforcement LearningContinuous ControlActor-Free Continuous Control via Structurally Maximizable Q-Functions
Value-based algorithms are a cornerstone of off-policy reinforcement learning due to their simplicity and training stability. However, their use has traditionally been restricted to discrete action spaces, as they rely o…
Reinforcement LearningContinuous ControlSelf-Evidencing Through Hierarchical Gradient Decomposition: A Dissipative System That Maintains Non-Equilibrium Steady-State by Minimizing Variational Free Energy
The Free Energy Principle (FEP) states that self-organizing systems must minimize variational free energy to persist, but the path from principle to implementable algorithm has remained unclear. We present a constructive…
Reinforcement LearningContinuous ControlA New Perspective on Transformers in Online Reinforcement Learning for Continuous Control
Despite their effectiveness and popularity in offline or model-based reinforcement learning (RL), transformers remain underexplored in online model-free RL due to their sensitivity to training setups and model design dec…
Reinforcement LearningContinuous ControlPrecise Attribute Intensity Control in Large Language Models via Targeted Representation Editing
Precise attribute intensity control--generating Large Language Model (LLM) outputs with specific, user-defined attribute intensities--is crucial for AI systems adaptable to diverse user expectations. Current LLM alignmen…
Continuous ControlText GenerationA Primer on SO(3) Action Representations in Deep Reinforcement Learning
Many robotic control tasks require policies to act on orientations, yet the geometry of SO(3) makes this nontrivial. Because SO(3) admits no global, smooth, minimal parameterization, common representations such as Euler …
Reinforcement LearningContinuous ControlADARL: Adaptive Low-Rank Structures for Robust Policy Learning under Uncertainty
Robust reinforcement learning (Robust RL) seeks to handle epistemic uncertainty in environment dynamics, but existing approaches often rely on nested min--max optimization, which is computationally expensive and yields o…
Reinforcement LearningContinuous ControlPopulation-Coded Spiking Neural Networks for High-Dimensional Robotic Control
Energy-efficient and high-performance motor control remains a critical challenge in robotics, particularly for high-dimensional continuous control tasks with limited onboard resources. While Deep Reinforcement Learning (…
Reinforcement LearningContinuous ControlPAC-Bayesian Reinforcement Learning Trains Generalizable Policies
We derive a novel PAC-Bayesian generalization bound for reinforcement learning that explicitly accounts for Markov dependencies in the data, through the chain's mixing time. This contributes to overcoming challenges in o…
Reinforcement LearningContinuous ControlTaxonomy and Trends in Reinforcement Learning for Robotics and Control Systems: A Structured Review
Reinforcement learning (RL) has become a foundational approach for enabling intelligent robotic behavior in dynamic and uncertain environments. This work presents an in-depth review of RL principles, advanced deep reinfo…
Reinforcement LearningContinuous ControlEntropy Regularizing Activation: Boosting Continuous Control, Large Language Models, and Image Classification with Activation as Entropy Constraints
We propose ERA, a new paradigm that constrains the sampling entropy above given thresholds by applying specially designed activations to the outputs of models. Our approach demonstrates broad effectiveness across differe…
Reinforcement LearningImage ClassificationContinuous Control