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 1426–1450 of 15113 papers

TitleStatusHype
MarineFormer: A Spatio-Temporal Attention Model for USV Navigation in Dynamic Marine Environments—0
Fine-Tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein DesignCode2
Truncating Trajectories in Monte Carlo Policy Evaluation: an Adaptive Approach—0
ORSO: Accelerating Reward Design via Online Reward Selection and Policy OptimizationCode0
Guided Reinforcement Learning for Robust Multi-Contact Loco-Manipulation—0
Integrating Large Language Models and Reinforcement Learning for Non-Linear Reasoning—0
Augmented Intelligence in Smart Intersections: Local Digital Twins-Assisted Hybrid Autonomous Driving—0
Reinforcement Learning with Euclidean Data Augmentation for State-Based Continuous ControlCode0
Sample-Efficient Reinforcement Learning with Temporal Logic Objectives: Leveraging the Task Specification to Guide ExplorationCode0
Off-dynamics Conditional Diffusion Planners—0
EdgeRL: Reinforcement Learning-driven Deep Learning Model Inference Optimization at Edge—0
Dynamic Learning Rate for Deep Reinforcement Learning: A Bandit Approach—0
Neural-based Control for CubeSat Docking Maneuvers—0
SAC-GLAM: Improving Online RL for LLM agents with Soft Actor-Critic and Hindsight Relabeling—0
Reinforcement Learning with LTL and ω-Regular Objectives via Optimality-Preserving Translation to Average Rewards—0
When to Trust Your Data: Enhancing Dyna-Style Model-Based Reinforcement Learning With Data Filter—0
Robust RL with LLM-Driven Data Synthesis and Policy Adaptation for Autonomous Driving—0
Multi-Objective-Optimization Multi-AUV Assisted Data Collection Framework for IoUT Based on Offline Reinforcement Learning—0
Reinforcement Learning Based Bidding Framework with High-dimensional Bids in Power Markets—0
Safety Filtering While Training: Improving the Performance and Sample Efficiency of Reinforcement Learning AgentsCode1
Multi-objective Reinforcement Learning: A Tool for Pluralistic Alignment—0
Meta-DT: Offline Meta-RL as Conditional Sequence Modeling with World Model DisentanglementCode2
Diffusion-Based Offline RL for Improved Decision-Making in Augmented ARC Task—0
Bayes Adaptive Monte Carlo Tree Search for Offline Model-based Reinforcement LearningCode0
ILAEDA: An Imitation Learning Based Approach for Automatic Exploratory Data Analysis—0
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Benchmark Results

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