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 3891–3900 of 15113 papers

TitleStatusHype
Towards interpretable quantum machine learning via single-photon quantum walks—0
Retrosynthetic Planning with Dual Value NetworksCode1
Scaling laws for single-agent reinforcement learning—0
CRC-RL: A Novel Visual Feature Representation Architecture for Unsupervised Reinforcement LearningCode0
Execution-based Code Generation using Deep Reinforcement LearningCode1
Learning, Fast and Slow: A Goal-Directed Memory-Based Approach for Dynamic EnvironmentsCode1
Partitioning Distributed Compute Jobs with Reinforcement Learning and Graph Neural Networks—0
Importance Weighted Actor-Critic for Optimal Conservative Offline Reinforcement LearningCode0
STEEL: Singularity-aware Reinforcement Learning—0
PAC-Bayesian Soft Actor-Critic LearningCode0
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Benchmark Results

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