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 2591–2600 of 15113 papers

TitleStatusHype
IN-RIL: Interleaved Reinforcement and Imitation Learning for Policy Fine-TuningCode0
Reinforcing the Diffusion Chain of Lateral Thought with Diffusion Language Models—0
Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation—0
TensorRL-QAS: Reinforcement learning with tensor networks for scalable quantum architecture search—0
Reinforcement Learning for Individual Optimal Policy from Heterogeneous Data—0
Risk-Aware Safe Reinforcement Learning for Control of Stochastic Linear Systems—0
CEC-Zero: Chinese Error Correction Solution Based on LLM—0
Adaptive Security Policy Management in Cloud Environments Using Reinforcement Learning—0
Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning—0
Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles—0
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Benchmark Results

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