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 4341–4350 of 15113 papers

TitleStatusHype
Learning from Imperfect Demonstrations with Self-Supervision for Robotic Manipulation—0
PRewrite: Prompt Rewriting with Reinforcement Learning—0
Learning from Sparse Offline Datasets via Conservative Density EstimationCode0
CycLight: learning traffic signal cooperation with a cycle-level strategy—0
Safe Reinforcement Learning with Free-form Natural Language Constraints and Pre-Trained Language Models—0
Reinforcement Learning from LLM Feedback to Counteract Goal Misgeneralization—0
Beyond Sparse Rewards: Enhancing Reinforcement Learning with Language Model Critique in Text Generation—0
Discovering Command and Control Channels Using Reinforcement Learning—0
BP(λ): Online Learning via Synthetic Gradients—0
Mutual Enhancement of Large Language and Reinforcement Learning Models through Bi-Directional Feedback Mechanisms: A Case Study—0
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Benchmark Results

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