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 1101–1110 of 15113 papers

TitleStatusHype
Proposing Hierarchical Goal-Conditioned Policy Planning in Multi-Goal Reinforcement Learning—0
Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement LearningCode0
RaSS: Improving Denoising Diffusion Samplers with Reinforced Active Sampling Scheduler—0
Neural Motion Simulator Pushing the Limit of World Models in Reinforcement Learning—0
A Graphical Approach to State Variable Selection in Off-policy Learning—0
Hybridising Reinforcement Learning and Heuristics for Hierarchical Directed Arc Routing ProblemsCode0
FORM: Learning Expressive and Transferable First-Order Logic Reward Machines—0
Towards Unraveling and Improving Generalization in World Models—0
Weber-Fechner Law in Temporal Difference learning derived from Control as Inference—0
Isoperimetry is All We Need: Langevin Posterior Sampling for RL with Sublinear Regret—0
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Benchmark Results

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