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 1021–1030 of 15113 papers

TitleStatusHype
Analytic Manifold Learning: Unifying and Evaluating Representations for Continuous ControlCode1
Conservative and Adaptive Penalty for Model-Based Safe Reinforcement LearningCode1
Reliable Conditioning of Behavioral Cloning for Offline Reinforcement LearningCode1
Constrained Variational Policy Optimization for Safe Reinforcement LearningCode1
Advancing Multimodal Reasoning via Reinforcement Learning with Cold StartCode1
ConfuciuX: Autonomous Hardware Resource Assignment for DNN Accelerators using Reinforcement LearningCode1
Confidence Estimation Transformer for Long-term Renewable Energy Forecasting in Reinforcement Learning-based Power Grid DispatchingCode1
Connecting Deep-Reinforcement-Learning-based Obstacle Avoidance with Conventional Global Planners using Waypoint GeneratorsCode1
Computational Performance of Deep Reinforcement Learning to find Nash EquilibriaCode1
Compound AI Systems Optimization: A Survey of Methods, Challenges, and Future DirectionsCode1
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Benchmark Results

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