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 2551–2560 of 15113 papers

TitleStatusHype
Safe Reinforcement Learning with Free-form Natural Language Constraints and Pre-Trained Language Models—0
Beyond Sparse Rewards: Enhancing Reinforcement Learning with Language Model Critique in Text Generation—0
Reinforcement Learning from LLM Feedback to Counteract Goal Misgeneralization—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
UNEX-RL: Reinforcing Long-Term Rewards in Multi-Stage Recommender Systems with UNidirectional EXecution—0
Model-Free Reinforcement Learning for Automated Fluid Administration in Critical Care—0
Interpretable Concept Bottlenecks to Align Reinforcement Learning AgentsCode1
Improving Large Language Models via Fine-grained Reinforcement Learning with Minimum Editing ConstraintCode1
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Benchmark Results

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