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 2781–2790 of 15113 papers

TitleStatusHype
Multi-Agent Reinforcement Learning for Graph Discovery in D2D-Enabled Federated Learning—0
Entropy-guided sequence weighting for efficient exploration in RL-based LLM fine-tuning—0
FLAM: Foundation Model-Based Body Stabilization for Humanoid Locomotion and Manipulation—0
Reinforcement Learning for Machine Learning Model Deployment: Evaluating Multi-Armed Bandits in ML Ops Environments—0
Controlling Large Language Model with Latent ActionsCode0
Reward Design for Reinforcement Learning AgentsCode0
Pretrained Bayesian Non-parametric Knowledge Prior in Robotic Long-Horizon Reinforcement LearningCode0
Bresa: Bio-inspired Reflexive Safe Reinforcement Learning for Contact-Rich Robotic Tasks—0
Learning Adaptive Dexterous Grasping from Single Demonstrations—0
Reasoning Beyond Limits: Advances and Open Problems for LLMs—0
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Benchmark Results

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