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Continuous Control
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Continuous control in the context of playing games, especially within artificial intelligence (AI) and machine learning (ML), refers to the ability to make a series of smooth, ongoing adjustments or actions to control a game or a simulation. This is in contrast to discrete control, where the actions are limited to a set of specific, distinct choices. Continuous control is crucial in environments where precision, timing, and the magnitude of actions matter, such as driving a car in a racing game, controlling a character in a simulation, or managing the flight of an aircraft in a flight simulator.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
rQdia: Regularizing Q-Value Distributions With Image Augmentation
Sparse-Reg: Improving Sample Complexity in Offline Reinforcement Learning using Sparsity
Fractional Reasoning via Latent Steering Vectors Improves Inference Time Compute
Scaling Algorithm Distillation for Continuous Control with Mamba
DR-SAC: Distributionally Robust Soft Actor-Critic for Reinforcement Learning under Uncertainty
Wasserstein Barycenter Soft Actor-Critic
Reinforcement Learning via Implicit Imitation Guidance
BEAST: Efficient Tokenization of B-Splines Encoded Action Sequences for Imitation Learning
Safe Planning and Policy Optimization via World Model Learning
AutoQD: Automatic Discovery of Diverse Behaviors with Quality-Diversity Optimization
Unsupervised Meta-Testing with Conditional Neural Processes for Hybrid Meta-Reinforcement Learning
Self-Composing Policies for Scalable Continual Reinforcement Learning
Proxy Target: Bridging the Gap Between Discrete Spiking Neural Networks and Continuous Control
Equivalence of stochastic and deterministic policy gradients
Knowledge Insulating Vision-Language-Action Models: Train Fast, Run Fast, Generalize Better
DATD3: Depthwise Attention Twin Delayed Deep Deterministic Policy Gradient For Model Free Reinforcement Learning Under Output Feedback Control
Improving Value Estimation Critically Enhances Vanilla Policy Gradient
RLBenchNet: The Right Network for the Right Reinforcement Learning Task
Guided Policy Optimization under Partial Observability
AM-PPO: (Advantage) Alpha-Modulation with Proximal Policy Optimization
World Models as Reference Trajectories for Rapid Motor Adaptation
KIPPO: Koopman-Inspired Proximal Policy Optimization
Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation
CIE: Controlling Language Model Text Generations Using Continuous Signals
Bi-Level Policy Optimization with Nyström Hypergradients
Adaptive Diffusion Policy Optimization for Robotic Manipulation
Monte Carlo Beam Search for Actor-Critic Reinforcement Learning in Continuous Control
Cache-Efficient Posterior Sampling for Reinforcement Learning with LLM-Derived Priors Across Discrete and Continuous Domains
Offline Multi-agent Reinforcement Learning via Score Decomposition
CLAM: Continuous Latent Action Models for Robot Learning from Unlabeled Demonstrations
Enhanced Robust Tracking Control: An Online Learning Approach
Policy-labeled Preference Learning: Is Preference Enough for RLHF?
Surrogate Fitness Metrics for Interpretable Reinforcement Learning
TraCeS: Trajectory Based Credit Assignment From Sparse Safety Feedback
A Hybrid Transmitting and Reflecting Beyond Diagonal Reconfigurable Intelligent Surface with Independent Beam Control and Power Splitting
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