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 4571–4580 of 15113 papers

TitleStatusHype
Grow Your Limits: Continuous Improvement with Real-World RL for Robotic Locomotion—0
Counterfactual-Augmented Importance Sampling for Semi-Offline Policy EvaluationCode0
Demonstration-Regularized RL—0
CQM: Curriculum Reinforcement Learning with a Quantized World Model—0
Understanding when Dynamics-Invariant Data Augmentations Benefit Model-Free Reinforcement Learning UpdatesCode0
Model-enhanced Contrastive Reinforcement Learning for Sequential Recommendation—0
Transfer of Reinforcement Learning-Based Controllers from Model- to Hardware-in-the-Loop—0
Privately Aligning Language Models with Reinforcement Learning—0
MultiPrompter: Cooperative Prompt Optimization with Multi-Agent Reinforcement Learning—0
Controlled Decoding from Language Models—0
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Benchmark Results

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