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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3081–3090 of 15113 papers

TitleStatusHype
Extend Adversarial Policy Against Neural Machine Translation via Unknown Token—0
RL-RC-DoT: A Block-level RL agent for Task-Aware Video Compression—0
Reinforcement Learning Constrained Beam Search for Parameter Optimization of Paper Drying Under Flexible Constraints—0
RedStar: Does Scaling Long-CoT Data Unlock Better Slow-Reasoning Systems?—0
Improving thermal state preparation of Sachdev-Ye-Kitaev model with reinforcement learning on quantum hardwareCode0
GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code GenerationCode0
Solving Finite-Horizon MDPs via Low-Rank Tensors—0
Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models—0
RE-POSE: Synergizing Reinforcement Learning-Based Partitioning and Offloading for Edge Object Detection—0
From Explainability to Interpretability: Interpretable Policies in Reinforcement Learning Via Model Explanation—0
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Benchmark Results

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