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 1141–1150 of 15113 papers

TitleStatusHype
Conservative Offline Distributional Reinforcement LearningCode1
Conservative Q-Learning for Offline Reinforcement LearningCode1
Consistent Paths Lead to Truth: Self-Rewarding Reinforcement Learning for LLM ReasoningCode1
Compiler Optimization for Quantum Computing Using Reinforcement LearningCode1
A Deep Reinforcement Learning Approach to First-Order Logic Theorem ProvingCode1
Constrained Policy Optimization via Bayesian World ModelsCode1
Constraint-Guided Reinforcement Learning: Augmenting the Agent-Environment-InteractionCode1
Constructions in combinatorics via neural networksCode1
Content Masked Loss: Human-Like Brush Stroke Planning in a Reinforcement Learning Painting AgentCode1
Compound AI Systems Optimization: A Survey of Methods, Challenges, and Future DirectionsCode1
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Benchmark Results

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