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 1191–1200 of 15113 papers

TitleStatusHype
Blockchain Framework for Artificial Intelligence ComputationCode1
Bidirectional Model-based Policy OptimizationCode1
Eigenoption Discovery through the Deep Successor RepresentationCode1
Efficient Symptom Inquiring and Diagnosis via Adaptive Alignment of Reinforcement Learning and ClassificationCode1
Bingham Policy Parameterization for 3D Rotations in Reinforcement LearningCode1
Widening the Pipeline in Human-Guided Reinforcement Learning with Explanation and Context-Aware Data AugmentationCode1
Exploiting Multimodal Reinforcement Learning for Simultaneous Machine TranslationCode1
Exploiting Transformer in Sparse Reward Reinforcement Learning for Interpretable Temporal Logic Motion PlanningCode1
Autonomous Exploration Under Uncertainty via Deep Reinforcement Learning on GraphsCode1
Trust Region-Based Safe Distributional Reinforcement Learning for Multiple ConstraintsCode1
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Benchmark Results

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