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 111–120 of 15113 papers

TitleStatusHype
imitation: Clean Imitation Learning ImplementationsCode3
Accelerating Goal-Conditioned RL Algorithms and ResearchCode3
Reinforcement Learning for Reasoning in Small LLMs: What Works and What Doesn'tCode3
Reinforcement Learning Meets Visual OdometryCode3
Generating Synergistic Formulaic Alpha Collections via Reinforcement LearningCode3
Graph-Reward-SQL: Execution-Free Reinforcement Learning for Text-to-SQL via Graph Matching and Stepwise RewardCode3
FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative FinanceCode3
Fine-Tuning Language Models from Human PreferencesCode3
Automatic Intrinsic Reward Shaping for Exploration in Deep Reinforcement LearningCode3
Flow Q-LearningCode3
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Benchmark Results

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