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 751–760 of 15113 papers

TitleStatusHype
CTSAC: Curriculum-Based Transformer Soft Actor-Critic for Goal-Oriented Robot Exploration—0
Med-R1: Reinforcement Learning for Generalizable Medical Reasoning in Vision-Language ModelsCode2
Enhancing LLM Reasoning with Iterative DPO: A Comprehensive Empirical InvestigationCode1
Synchronous vs Asynchronous Reinforcement Learning in a Real World Robot—0
APF+: Boosting adaptive-potential function reinforcement learning methods with a W-shaped network for high-dimensional games—0
FLEX: A Framework for Learning Robot-Agnostic Force-based Skills Involving Sustained Contact Object Manipulation—0
A Reinforcement Learning-Driven Transformer GAN for Molecular Generation—0
Dynamic Angle Selection in X-Ray CT: A Reinforcement Learning Approach to Optimal Stopping—0
TERL: Large-Scale Multi-Target Encirclement Using Transformer-Enhanced Reinforcement LearningCode1
Evaluation-Time Policy Switching for Offline Reinforcement Learning—0
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Benchmark Results

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