Papers Continuous Control
“Continuous Control” 태그가 달린 논문 1,432편 · 필터 해제
Approximate Next Policy Sampling: Replacing Conservative Target Policy Updates in Deep RL
We revisit a classic "chicken-and-egg" problem in reinforcement learning: to safely improve a policy, the value function must be accurate on the state-visitation distribution of the updated policy. That distribution over…
Reinforcement LearningContinuous ControlDiscovering Reinforcement Learning Interfaces with Large Language Models
Reinforcement learning systems rely on environment interfaces that specify observations and reward functions, yet constructing these interfaces for new tasks often requires substantial manual effort. While recent work ha…
Reinforcement LearningContinuous ControlSAVGO: Learning State-Action Value Geometry with Cosine Similarity for Continuous Control
While representation and similarity learning have improved the sample efficiency of Reinforcement Learning (RL), they are rarely used to shape policy updates directly in the action space. To bridge this gap, a geometry-a…
Representation LearningReinforcement LearningContinuous ControlCan Tabular Foundation Models Guide Exploration in Robot Policy Learning?
Policy optimization in high-dimensional continuous control for robotics remains a challenging problem. Predominant methods are inherently local and often require extensive tuning and carefully chosen initial guesses for …
Continuous ControlStyleShield: Exposing the Fragility of AIGC Detectors through Continuous Controllable Style Transfer
AI-generated content (AIGC) detectors are increasingly deployed in high-stakes settings such as academic integrity screening, yet their reliability rests on a fundamental paradox: as language models are trained on human-…
Text Style TransferSemantic SimilarityContinuous ControlUncertainty-Aware Reward Discounting for Mitigating Reward Hacking
Reinforcement learning from human feedback (RLHF) systems face a compounding alignment challenge: not only are learned reward models uncertain about unseen state-action pairs, but the human preference annotations they ar…
Reinforcement LearningContinuous ControlLength Value Model: Scalable Value Pretraining for Token-Level Length Modeling
Token serves as the fundamental unit of computation in modern autoregressive models, and generation length directly influences both inference cost and reasoning performance. Despite its importance, existing approaches la…
Continuous ControlMulti-action Tangled Program Graphs for Multi-task Reinforcement Learning with Continuous Control
Over the past few decades, machine learning has been widely used to learn complex tasks. Reinforcement Learning (RL), inspired by human behavior, is a great example, as it involves developing specific behaviours for spec…
Reinforcement LearningContinuous ControlTSN-Affinity: Similarity-Driven Parameter Reuse for Continual Offline Reinforcement Learning
Continual offline reinforcement learning (CORL) aims to learn a sequence of tasks from datasets collected over time while preserving performance on previously learned tasks. This setting corresponds to domains where new …
Reinforcement LearningContinuous ControlContinual LearningAtari GamesGIFT: Global stabilisation via Intrinsic Fine Tuning
Deep reinforcement learning policies achieve strong performance in complex continuous control environments with nonlinear contact forces. However, these policies often produce chaotic state dynamics, with trivially small…
Reinforcement LearningContinuous ControlCorridorVLA: Explicit Spatial Constraints for Generative Action Heads via Sparse Anchors
Vision--Language--Action (VLA) models often use intermediate representations to connect multimodal inputs with continuous control, yet spatial guidance is often injected implicitly through latent features. We propose Cor…
Continuous ControlInsect-inspired modular architectures as inductive biases for reinforcement learning
Most reinforcement-learning (RL) controllers used in continuous control are architecturally centralized: observations are compressed into a single latent state from which both value estimates and actions are produced. Bi…
Reinforcement LearningContinuous ControlPhase-Scheduled Multi-Agent Systems for Token-Efficient Coordination
Multi-agent systems (MAS) powered by large language models suffer from severe token inefficiency arising from two compounding sources: (i) unstructured parallel execution, where all agents activate simultaneously irrespe…
Continuous ControlIn-Context Learning Under Regime Change
Non-stationary sequences arise naturally in control, forecasting, and decision-making. The data-generating process shifts at unknown times, and models must detect the change, discard or downweight obsolete evidence, and …
Time Series ForecastingContinuous ControlTokenLight: Precise Lighting Control in Images using Attribute Tokens
This paper presents a method for image relighting that enables precise and continuous control over multiple illumination attributes in a photograph. We formulate relighting as a conditional image generation task and intr…
Conditional Image GenerationContinuous ControlInverse RenderingImage RelightingWOMBET: World Model-Based Experience Transfer for Robust and Sample-efficient Reinforcement Learning
Reinforcement learning (RL) in robotics is often limited by the cost and risk of data collection, motivating experience transfer from a source task to a target task. Offline-to-online RL leverages prior data but typicall…
Reinforcement LearningContinuous ControlModel-Based Reinforcement Learning for Control under Time-Varying Dynamics
Learning-based control methods typically assume stationary system dynamics, an assumption often violated in real-world systems due to drift, wear, or changing operating conditions. We study reinforcement learning for con…
Reinforcement LearningContinuous ControlTarget-Aligned Reinforcement Learning
Many value-based deep reinforcement learning algorithms rely on target networks - lagged copies of the online network - to stabilize training. While effective, this mechanism introduces a fundamental stability-recency tr…
Reinforcement LearningContinuous ControlA Pontryagin Method of Model-based Reinforcement Learning via Hamiltonian Actor-Critic
Model-based reinforcement learning (MBRL) improves sample efficiency by leveraging learned dynamics models for policy optimization. However, the effectiveness of methods such as actor-critic is often limited by compoundi…
Reinforcement LearningContinuous ControlOffline RLA Power-Weighted Noncentral Complex Gaussian Distribution
The complex Gaussian distribution has been widely used as a fundamental spectral and noise model in signal processing and communication. However, its Gaussian structure often limits its ability to represent the diverse a…
Continuous Control