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 4826–4850 of 15113 papers

TitleStatusHype
Reinforcement Learning for Battery Energy Storage Dispatch augmented with Model-based Optimizer—0
Reinforcement Learning for Beam Pattern Design in Millimeter Wave and Massive MIMO Systems—0
Reinforcement Learning for Better Verbalized Confidence in Long-Form Generation—0
Reinforcement Learning for Block Decomposition of CAD Models—0
Reinforcement Learning for Caching with Space-Time Popularity Dynamics—0
Reinforcement Learning for Classical Planning: Viewing Heuristics as Dense Reward Generators—0
Reinforcement Learning for Cognitive Delay/Disruption Tolerant Network Node Management in an LEO-based Satellite Constellation—0
Reinforcement Learning for Combinatorial Optimization: A Survey—0
Reinforcement Learning for ConnectX—0
Reinforcement Learning for Control with Probabilistic Stability Guarantee—0
Reinforcement Learning for Control with Multiple Frequencies—0
Reinforcement Learning for Datacenter Congestion Control—0
Reinforcement Learning For Data Poisoning on Graph Neural Networks—0
Reinforcement Learning for Deceiving Reactive Jammers in Wireless Networks—0
Multi-agent Reinforcement Learning for Decentralized Stable Matching—0
Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges—0
Reinforcement Learning for Distributed Transient Frequency Control with Stability and Safety Guarantees—0
Reinforcement Learning for Dynamic Resource Optimization in 5G Radio Access Network Slicing—0
Reinforcement Learning for Edit-Based Non-Autoregressive Neural Machine Translation—0
Reinforcement Learning for Education: Opportunities and Challenges—0
Reinforcement Learning for Efficient and Tuning-Free Link Adaptation—0
Reinforcement Learning for Efficient Design and Control Co-optimisation of Energy Systems—0
Reinforcement Learning for Electricity Network Operation—0
Reinforcement Learning for Emotional Text-to-Speech Synthesis with Improved Emotion Discriminability—0
Reinforcement Learning for Fair Dynamic Pricing—0
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Benchmark Results

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