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 3271–3280 of 15113 papers

TitleStatusHype
Combining Reinforcement Learning and Barrier Functions for Adaptive Risk Management in Portfolio Optimization—0
Online Prototype Alignment for Few-shot Policy TransferCode0
Transcendental Idealism of Planner: Evaluating Perception from Planning Perspective for Autonomous DrivingCode1
Tackling Heavy-Tailed Rewards in Reinforcement Learning with Function Approximation: Minimax Optimal and Instance-Dependent Regret Bounds—0
ENOTO: Improving Offline-to-Online Reinforcement Learning with Q-Ensembles—0
Diverse Projection Ensembles for Distributional Reinforcement Learning—0
Reinforcement Learning in Robotic Motion Planning by Combined Experience-based Planning and Self-Imitation Learning—0
Digital Twin-Enhanced Wireless Indoor Navigation: Achieving Efficient Environment Sensing with Zero-Shot Reinforcement LearningCode1
Policy Regularization with Dataset Constraint for Offline Reinforcement LearningCode1
PEAR: Primitive enabled Adaptive Relabeling for boosting Hierarchical Reinforcement Learning—0
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Benchmark Results

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