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 1091–1100 of 15113 papers

TitleStatusHype
Digital Twin Aided Channel Estimation: Zone-Specific Subspace Prediction and CalibrationCode0
Learn A Flexible Exploration Model for Parameterized Action Markov Decision Processes—0
Interpretable Recognition of Fused Magnesium Furnace Working Conditions with Deep Convolutional Stochastic Configuration Networks—0
Co-Activation Graph Analysis of Safety-Verified and Explainable Deep Reinforcement Learning PoliciesCode1
Sim-to-Real Transfer for Mobile Robots with Reinforcement Learning: from NVIDIA Isaac Sim to Gazebo and Real ROS 2 RobotsCode2
AMM: Adaptive Modularized Reinforcement Model for Multi-city Traffic Signal Control—0
Representation Convergence: Mutual Distillation is Secretly a Form of RegularizationCode0
A New Interpretation of the Certainty-Equivalence Approach for PAC Reinforcement Learning with a Generative Model—0
SR-Reward: Taking The Path More Traveled—0
On the Statistical Complexity for Offline and Low-Adaptive Reinforcement Learning with Structures—0
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Benchmark Results

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