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 2301–2310 of 15113 papers

TitleStatusHype
Beam Management Driven by Radio Environment Maps in O-RAN Architecture—0
BeamDojo: Learning Agile Humanoid Locomotion on Sparse Footholds—0
Analysis of Randomization Effects on Sim2Real Transfer in Reinforcement Learning for Robotic Manipulation Tasks—0
BCRLSP: An Offline Reinforcement Learning Framework for Sequential Targeted Promotion—0
Analysis of Information Propagation in Ethereum Network Using Combined Graph Attention Network and Reinforcement Learning to Optimize Network Efficiency and Scalability—0
Adaptive Trade-Offs in Off-Policy Learning—0
Taming Reachability Analysis of DNN-Controlled Systems via Abstraction-Based Training—0
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems—0
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems—0
Analysis of Evolutionary Behavior in Self-Learning Media Search Engines—0
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Benchmark Results

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