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 4471–4480 of 15113 papers

TitleStatusHype
Self-Improving Safety Performance of Reinforcement Learning Based Driving with Black-Box Verification AlgorithmsCode1
LearningGroup: A Real-Time Sparse Training on FPGA via Learnable Weight Grouping for Multi-Agent Reinforcement Learning—0
BIMRL: Brain Inspired Meta Reinforcement LearningCode1
DeFIX: Detecting and Fixing Failure Scenarios with Reinforcement Learning in Imitation Learning Based Autonomous DrivingCode1
Reinforcement Learning-based Defect Mitigation for Quality Assurance of Additive Manufacturing—0
Using Contrastive Samples for Identifying and Leveraging Possible Causal Relationships in Reinforcement Learning—0
Nonuniqueness and Convergence to Equivalent Solutions in Observer-based Inverse Reinforcement Learning—0
Goal Exploration Augmentation via Pre-trained Skills for Sparse-Reward Long-Horizon Goal-Conditioned Reinforcement LearningCode0
Hybrid Indoor Localization via Reinforcement Learning-based Information Fusion—0
Language Control Diffusion: Efficiently Scaling through Space, Time, and TasksCode1
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Benchmark Results

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