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 4051–4060 of 15113 papers

TitleStatusHype
Stabilizing Visual Reinforcement Learning via Asymmetric Interactive Cooperation—0
PolicyCleanse: Backdoor Detection and Mitigation for Competitive Reinforcement Learning—0
Simoun: Synergizing Interactive Motion-appearance Understanding for Vision-based Reinforcement Learning—0
Co-Speech Gesture Synthesis by Reinforcement Learning With Contrastive Pre-Trained RewardsCode0
Local-Guided Global: Paired Similarity Representation for Visual Reinforcement Learning—0
Second Thoughts are Best: Learning to Re-Align With Human Values from Text Edits—0
Optimization of Image Transmission in a Cooperative Semantic Communication Networks—0
Goal-Guided Transformer-Enabled Reinforcement Learning for Efficient Autonomous NavigationCode1
Cost-Effective Two-Stage Network Slicing for Edge-Cloud Orchestrated Vehicular Networks—0
Accuracy-Guaranteed Collaborative DNN Inference in Industrial IoT via Deep Reinforcement Learning—0
Show:102550
← PrevPage 406 of 1512Next →

Benchmark Results

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