SOTAVerified

Continuous Control

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.

Papers

Showing 301350 of 1161 papers

TitleStatusHype
Diminishing Return of Value Expansion Methods in Model-Based Reinforcement LearningCode1
MAESTRO: Open-Ended Environment Design for Multi-Agent Reinforcement Learning0
Swim: A General-Purpose, High-Performing, and Efficient Activation Function for Locomotion Control TasksCode0
Offline Imitation Learning with Suboptimal Demonstrations via Relaxed Distribution Matching0
CFlowNets: Continuous Control with Generative Flow NetworksCode0
Guarded Policy Optimization with Imperfect Online Demonstrations0
Planning and Control of Uncertain Cooperative Mobile Manipulator-Endowed Systems under Temporal-Logic Tasks0
Resource-Constrained Station-Keeping for Helium Balloons using Reinforcement Learning0
Hallucinated Adversarial Control for Conservative Offline Policy EvaluationCode0
LS-IQ: Implicit Reward Regularization for Inverse Reinforcement LearningCode1
Auxiliary Task-based Deep Reinforcement Learning for Quantum Control0
CrystalBox: Future-Based Explanations for Input-Driven Deep RL SystemsCode0
Continuous descriptor-based control for deep audio synthesisCode1
Diffusion Model-Augmented Behavioral Cloning0
Model-Based Uncertainty in Value FunctionsCode1
To the Noise and Back: Diffusion for Shared Autonomy0
Universal Morphology Control via Contextual ModulationCode1
Improving Deep Policy Gradients with Value Function Search0
When Demonstrations Meet Generative World Models: A Maximum Likelihood Framework for Offline Inverse Reinforcement LearningCode1
CLARE: Conservative Model-Based Reward Learning for Offline Inverse Reinforcement Learning0
Zero-shot Sim2Real Adaptation Across Environments0
Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority InfluenceCode1
A Strong Baseline for Batch Imitation Learning0
ReLOAD: Reinforcement Learning with Optimistic Ascent-Descent for Last-Iterate Convergence in Constrained MDPs0
Optimal Transport Perturbations for Safe Reinforcement Learning with Robustness GuaranteesCode1
Revisiting Estimation Bias in Policy Gradients for Deep Reinforcement Learning0
On The Fragility of Learned Reward Functions0
Centralized Cooperative Exploration Policy for Continuous Control TasksCode0
Learning Goal-Conditioned Policies Offline with Self-Supervised Reward ShapingCode1
Robust Control for Dynamical Systems With Non-Gaussian Noise via Formal AbstractionsCode0
Imitation Learning As State Matching via Differentiable Physics0
Offline Policy Optimization in RL with Variance Regularizaton0
Invariance to Quantile Selection in Distributional Continuous Control0
Temporally Layered Architecture for Adaptive, Distributed and Continuous Control0
Variational Quantum Soft Actor-Critic for Robotic Arm Control0
Managing Temporal Resolution in Continuous Value Estimation: A Fundamental Trade-off0
A Simple Decentralized Cross-Entropy MethodCode0
Robust Policy Optimization in Deep Reinforcement LearningCode0
PPO-UE: Proximal Policy Optimization via Uncertainty-Aware Exploration0
On the Sensitivity of Reward Inference to Misspecified Human Models0
Accelerating Self-Imitation Learning from Demonstrations via Policy Constraints and Q-Ensemble0
First Go, then Post-Explore: the Benefits of Post-Exploration in Intrinsic Motivation0
Dynamic Decision Frequency with Continuous OptionsCode0
Q-Pensieve: Boosting Sample Efficiency of Multi-Objective RL Through Memory Sharing of Q-SnapshotsCode0
Policy Learning for Active Target Tracking over Continuous SE(3) TrajectoriesCode1
STL-Based Synthesis of Feedback Controllers Using Reinforcement LearningCode0
Quadratic Programming for Continuous Control of Safety-Critical Multi-Agent Systems Under Uncertainty0
Continuous Neural Algorithmic Planners0
Learning from Good Trajectories in Offline Multi-Agent Reinforcement Learning0
Hypernetworks for Zero-shot Transfer in Reinforcement Learning0
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