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 1391–1400 of 15113 papers

TitleStatusHype
Learn 2 Rage: Experiencing The Emotional Roller Coaster That Is Reinforcement Learning—0
Optimizing Load Scheduling in Power Grids Using Reinforcement Learning and Markov Decision Processes—0
The Hive Mind is a Single Reinforcement Learning Agent—0
Primal-Dual Spectral Representation for Off-policy Evaluation—0
Leveraging Skills from Unlabeled Prior Data for Efficient Online ExplorationCode1
Dynamic Spectrum Access for Ambient Backscatter Communication-assisted D2D Systems with Quantum Reinforcement Learning—0
Process Supervision-Guided Policy Optimization for Code Generation—0
Learning Versatile Skills with Curriculum MaskingCode0
Multi-Modal Transformer and Reinforcement Learning-based Beam Management—0
Survival of the Fittest: Evolutionary Adaptation of Policies for Environmental Shifts—0
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Benchmark Results

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