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 841850 of 15113 papers

TitleStatusHype
Energy-Based Imitation LearningCode1
Energy-Guided Diffusion Sampling for Offline-to-Online Reinforcement LearningCode1
ENERO: Efficient Real-Time WAN Routing Optimization with Deep Reinforcement LearningCode1
Enforcing Policy Feasibility Constraints through Differentiable Projection for Energy OptimizationCode1
Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their SolutionsCode1
Enhancement of a state-of-the-art RL-based detection algorithm for Massive MIMO radarsCode1
Conservative and Adaptive Penalty for Model-Based Safe Reinforcement LearningCode1
Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged FraudstersCode1
Reliable Conditioning of Behavioral Cloning for Offline Reinforcement LearningCode1
Confidence Estimation Transformer for Long-term Renewable Energy Forecasting in Reinforcement Learning-based Power Grid DispatchingCode1
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Benchmark Results

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