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 3061–3070 of 15113 papers

TitleStatusHype
Flexible Blood Glucose Control: Offline Reinforcement Learning from Human Feedback—0
Benchmarking Quantum Reinforcement LearningCode0
Expert-Free Online Transfer Learning in Multi-Agent Reinforcement LearningCode0
Learning-Enhanced Safeguard Control for High-Relative-Degree Systems: Robust Optimization under Disturbances and Faults—0
Data Center Cooling System Optimization Using Offline Reinforcement Learning—0
Faster Machine Translation Ensembling with Reinforcement Learning and Competitive Correction—0
Coordinating Ride-Pooling with Public Transit using Reward-Guided Conservative Q-Learning: An Offline Training and Online Fine-Tuning Reinforcement Learning Framework—0
Age and Power Minimization via Meta-Deep Reinforcement Learning in UAV Networks—0
Towards Efficient Multi-Objective Optimisation for Real-World Power Grid Topology Control—0
Large Language Model driven Policy Exploration for Recommender Systems—0
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Benchmark Results

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