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 1501–1510 of 15113 papers

TitleStatusHype
Maximum Entropy Population-Based Training for Zero-Shot Human-AI CoordinationCode1
Maximum Entropy-Regularized Multi-Goal Reinforcement LearningCode1
GEAR: A GPU-Centric Experience Replay System for Large Reinforcement Learning ModelsCode1
Deep Reinforcement Learning with Gradient Eligibility TracesCode1
Measuring Visual Generalization in Continuous Control from PixelsCode1
A Deep Reinforcement Learning Algorithm Using Dynamic Attention Model for Vehicle Routing ProblemsCode1
A Deep Reinforced Model for Zero-Shot Cross-Lingual Summarization with Bilingual Semantic Similarity RewardsCode1
MELD: Meta-Reinforcement Learning from Images via Latent State ModelsCode1
Tactical Optimism and Pessimism for Deep Reinforcement LearningCode1
Forgetful Experience Replay in Hierarchical Reinforcement Learning from DemonstrationsCode1
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Benchmark Results

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