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

Showing 10711080 of 15113 papers

TitleStatusHype
SMPL: Simulated Industrial Manufacturing and Process Control Learning EnvironmentsCode1
Barrier Certified Safety Learning Control: When Sum-of-Square Programming Meets Reinforcement LearningCode1
A Search-Based Testing Approach for Deep Reinforcement Learning AgentsCode1
Training Discrete Deep Generative Models via Gapped Straight-Through EstimatorCode1
RoSGAS: Adaptive Social Bot Detection with Reinforced Self-Supervised GNN Architecture SearchCode1
Transformers are Meta-Reinforcement LearnersCode1
Reinforcement Learning-based Placement of Charging Stations in Urban Road NetworksCode1
A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum GamesCode1
ROI-Constrained Bidding via Curriculum-Guided Bayesian Reinforcement LearningCode1
A Relational Intervention Approach for Unsupervised Dynamics Generalization in Model-Based Reinforcement LearningCode1
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Benchmark Results

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