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 1671–1680 of 15113 papers

TitleStatusHype
SUMO: Search-Based Uncertainty Estimation for Model-Based Offline Reinforcement Learning—0
Query-Efficient Video Adversarial Attack with Stylized Logo—0
Leveraging Unlabeled Data Sharing through Kernel Function Approximation in Offline Reinforcement LearningCode0
Balancing Act: Prioritization Strategies for LLM-Designed Restless Bandit Rewards—0
Domain Adaptation for Offline Reinforcement Learning with Limited Samples—0
Advances in Preference-based Reinforcement Learning: A Review—0
Using Part-based Representations for Explainable Deep Reinforcement Learning—0
Offline Model-Based Reinforcement Learning with Anti-Exploration—0
Hokoff: Real Game Dataset from Honor of Kings and its Offline Reinforcement Learning BenchmarksCode2
Accelerating Goal-Conditioned RL Algorithms and ResearchCode3
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Benchmark Results

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