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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 6451–6475 of 15113 papers

TitleStatusHype
TensorRL-QAS: Reinforcement learning with tensor networks for scalable quantum architecture search—0
Terminal Adaptive Guidance for Autonomous Hypersonic Strike Weapons via Reinforcement Learning—0
Terminal Prediction as an Auxiliary Task for Deep Reinforcement Learning—0
Tesseract: Tensorised Actors for Multi-Agent Reinforcement Learning—0
Test-Cost Sensitive Methods for Identifying Nearby Points—0
Testing match-3 video games with Deep Reinforcement Learning—0
Test Where Decisions Matter: Importance-driven Testing for Deep Reinforcement Learning—0
TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning—0
Text as Environment: A Deep Reinforcement Learning Text Readability Assessment Model—0
Text-Based Interactive Recommendation via Constraint-Augmented Reinforcement Learning—0
Text-based RL Agents with Commonsense Knowledge: New Challenges, Environments and Approaches—0
TextDiffuser-RL: Efficient and Robust Text Layout Optimization for High-Fidelity Text-to-Image Synthesis—0
Text Generation with Efficient (Soft) Q-Learning—0
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
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Benchmark Results

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