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 1861–1870 of 15113 papers

TitleStatusHype
Leveraging Reinforcement Learning in Red Teaming for Advanced Ransomware Attack Simulations—0
Decentralized RL-Based Data Transmission Scheme for Energy Efficient Harvesting—0
Tolerance of Reinforcement Learning Controllers against Deviations in Cyber Physical Systems—0
Reinforcement Learning via Auxiliary Task DistillationCode0
Memory-Enhanced Neural Solvers for Efficient Adaptation in Combinatorial OptimizationCode1
KEHRL: Learning Knowledge-Enhanced Language Representations with Hierarchical Reinforcement LearningCode0
Uncertainty-Aware Reward-Free Exploration with General Function ApproximationCode0
OCALM: Object-Centric Assessment with Language Models—0
Confidence Aware Inverse Constrained Reinforcement LearningCode0
Enhancing Commentary Strategies for Imperfect Information Card Games: A Study of Large Language Models in Guandan CommentaryCode0
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Benchmark Results

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