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 1381–1390 of 15113 papers

TitleStatusHype
On-Robot Reinforcement Learning with Goal-Contrastive Rewards—0
Provably Adaptive Average Reward Reinforcement Learning for Metric Spaces—0
Reinforcement Learning for Aligning Large Language Models Agents with Interactive Environments: Quantifying and Mitigating Prompt Overfitting—0
Random Policy Enables In-Context Reinforcement Learning within Trust Horizons—0
AgentForge: A Flexible Low-Code Platform for Reinforcement Learning Agent DesignCode0
Humanizing the Machine: Proxy Attacks to Mislead LLM Detectors—0
Adversarial Environment Design via Regret-Guided Diffusion Models—0
Offline Reinforcement Learning with OOD State Correction and OOD Action SuppressionCode1
Learn 2 Rage: Experiencing The Emotional Roller Coaster That Is Reinforcement Learning—0
PointPatchRL -- Masked Reconstruction Improves Reinforcement Learning on Point Clouds—0
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Benchmark Results

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