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 10701–10725 of 15113 papers

TitleStatusHype
FLAG-Trader: Fusion LLM-Agent with Gradient-based Reinforcement Learning for Financial Trading—0
Text Simplification with Reinforcement Learning Using Supervised Rewards on Grammaticality, Meaning Preservation, and Simplicity—0
FORM: Learning Expressive and Transferable First-Order Logic Reward Machines—0
That Escalated Quickly: Compounding Complexity by Editing Levels at the Frontier of Agent Capabilities—0
The act of remembering: a study in partially observable reinforcement learning—0
The Advantage Regret-Matching Actor-Critic—0
The Archimedean trap: Why traditional reinforcement learning will probably not yield AGI—0
The Architectural Implications of Distributed Reinforcement Learning on CPU-GPU Systems—0
The association problem in wireless networks: a Policy Gradient Reinforcement Learning approach—0
The Bandit Whisperer: Communication Learning for Restless Bandits—0
The Best of Both Worlds: Reinforcement Learning with Logarithmic Regret and Policy Switches—0
The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond—0
The Bottleneck Simulator: A Model-based Deep Reinforcement Learning Approach—0
The Case for Automatic Database Administration using Deep Reinforcement Learning—0
The Central Role of the Loss Function in Reinforcement Learning—0
The Challenges of Exploration for Offline Reinforcement Learning—0
The Complexity of Markov Equilibrium in Stochastic Games—0
The Complex Negotiation Dialogue Game—0
The Concept of Criticality in Reinforcement Learning—0
The Courage to Stop: Overcoming Sunk Cost Fallacy in Deep Reinforcement Learning—0
The Crucial Role of Problem Formulation in Real-World Reinforcement Learning—0
The Curious Price of Distributional Robustness in Reinforcement Learning with a Generative Model—0
The Differences Between Direct Alignment Algorithms are a Blur—0
The Difficulty of Passive Learning in Deep Reinforcement Learning—0
The Ecosystem Path to General AI—0
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Benchmark Results

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