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
Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning ApproachCode1
Efficient Reinforcement Learning via Decoupling Exploration and UtilizationCode1
Efficient Reinforcement Learning Through Trajectory GenerationCode1
Single-step deep reinforcement learning for open-loop control of laminar and turbulent flowsCode1
Efficient Risk-Averse Reinforcement LearningCode1
Efficient Symptom Inquiring and Diagnosis via Adaptive Alignment of Reinforcement Learning and ClassificationCode1
Gradient Imitation Reinforcement Learning for Low Resource Relation ExtractionCode1
Attractive or Faithful? Popularity-Reinforced Learning for Inspired Headline GenerationCode1
An empirical investigation of the challenges of real-world reinforcement learningCode1
Bayesian Action Decoder for Deep Multi-Agent Reinforcement LearningCode1
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Benchmark Results

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