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

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Showing 2901–2910 of 15113 papers

TitleStatusHype
Distributional Reinforcement Learning with Online Risk-awareness Adaption—0
DeepQTest: Testing Autonomous Driving Systems with Reinforcement Learning and Real-world Weather DataCode0
Learning Generalizable Agents via Saliency-Guided Features Decorrelation—0
GEAR: A GPU-Centric Experience Replay System for Large Reinforcement Learning ModelsCode1
Improving Offline-to-Online Reinforcement Learning with Q Conditioned State Entropy Exploration—0
Reinforced UI Instruction Grounding: Towards a Generic UI Task Automation API—0
Optimal Sequential Decision-Making in Geosteering: A Reinforcement Learning Approach—0
Beyond Uniform Sampling: Offline Reinforcement Learning with Imbalanced DatasetsCode1
Self-Confirming Transformer for Belief-Conditioned Adaptation in Offline Multi-Agent Reinforcement Learning—0
Self-Supervised Neuron Segmentation with Multi-Agent Reinforcement LearningCode1
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Benchmark Results

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