SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2851–2875 of 15113 papers

TitleStatusHype
Augmenting Control over Exploration Space in Molecular Dynamics Simulators to Streamline De Novo Analysis through Generative Control Policies—0
AceReason-Nemotron 1.1: Advancing Math and Code Reasoning through SFT and RL Synergy—0
Augmenting Automated Game Testing with Deep Reinforcement Learning—0
Augmented Replay Memory in Reinforcement Learning With Continuous Control—0
Data Quality-aware Mixed-precision Quantization via Hybrid Reinforcement Learning—0
Daylight: Assessing Generalization Skills of Deep Reinforcement Learning Agents—0
Modified DDPG car-following model with a real-world human driving experience with CARLA simulator—0
Augmented Random Search for Quadcopter Control: An alternative to Reinforcement Learning—0
Combining Multi-Objective Bayesian Optimization with Reinforcement Learning for TinyML—0
AITuning: Machine Learning-based Tuning Tool for Run-Time Communication Libraries—0
AISYN: AI-driven Reinforcement Learning-Based Logic Synthesis Framework—0
AUGMENTED POLICY GRADIENT METHODS FOR EFFICIENT REINFORCEMENT LEARNING—0
Adaptive Control of Differentially Private Linear Quadratic Systems—0
AAPO: Enhance the Reasoning Capabilities of LLMs with Advantage Momentum—0
Heterogeneous Knowledge for Augmented Modular Reinforcement Learning—0
Augmented Memory Networks for Streaming-Based Active One-Shot Learning—0
Adaptive Control of an Inverted Pendulum by a Reinforcement Learning-based LQR Method—0
Augmented Memory Networks for Streaming-Based Active One-Shot Learning—0
Augmented Lagrangian-Based Safe Reinforcement Learning Approach for Distribution System Volt/VAR Control—0
AirRL: A Reinforcement Learning Approach to Urban Air Quality Inference—0
ACERAC: Efficient reinforcement learning in fine time discretization—0
Augmented Intelligence in Smart Intersections: Local Digital Twins-Assisted Hybrid Autonomous Driving—0
A Two-Time-Scale Stochastic Optimization Framework with Applications in Control and Reinforcement Learning—0
AI Recommendation Systems for Lane-Changing Using Adherence-Aware Reinforcement Learning—0
A Two-stage Framework and Reinforcement Learning-based Optimization Algorithms for Complex Scheduling Problems—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified