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 3881–3890 of 15113 papers

TitleStatusHype
Bridging Physics-Informed Neural Networks with Reinforcement Learning: Hamilton-Jacobi-Bellman Proximal Policy Optimization (HJBPPO)—0
Internally Rewarded Reinforcement LearningCode1
Enabling surrogate-assisted evolutionary reinforcement learning via policy embedding—0
A Reinforcement Learning Framework for Dynamic Mediation AnalysisCode0
Optimizing DDPM Sampling with Shortcut Fine-TuningCode1
Skill Decision TransformerCode0
Scaling laws for single-agent reinforcement learning—0
Scalable Grid-Aware Dynamic Matching using Deep Reinforcement Learning—0
Few-Shot Image-to-Semantics Translation for Policy Transfer in Reinforcement LearningCode0
Partitioning Distributed Compute Jobs with Reinforcement Learning and Graph Neural Networks—0
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Benchmark Results

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