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 8901–8925 of 15113 papers

TitleStatusHype
Regularizing Trajectory Optimization with Denoising Autoencoders—0
Regulating Reward Training by Means of Certainty Prediction in a Neural Network-Implemented Pong Game—0
REIN-2: Giving Birth to Prepared Reinforcement Learning Agents Using Reinforcement Learning Agents—0
ReinDSplit: Reinforced Dynamic Split Learning for Pest Recognition in Precision Agriculture—0
ReinFlow: Fine-tuning Flow Matching Policy with Online Reinforcement Learning—0
Reinforce Attack: Adversarial Attack against BERT with Reinforcement Learning—0
Reinforced Anytime Bottom Up Rule Learning for Knowledge Graph Completion—0
Reinforced Bit Allocation under Task-Driven Semantic Distortion Metrics—0
Multi-Agent Reinforcement Learning for Network Load Balancing in Data Center—0
Reinforced Cross-Modal Matching and Self-Supervised Imitation Learning for Vision-Language Navigation—0
Reinforced dynamics for enhanced sampling in large atomic and molecular systems—0
Reinforced Extractive Summarization with Question-Focused Rewards—0
Reinforced Hybrid Genetic Algorithm for the Traveling Salesman Problem—0
Reinforced Imitation in Heterogeneous Action Space—0
Reinforced Imitation Learning by Free Energy Principle—0
Reinforced Inverse Scattering—0
Reinforced Labels: Multi-Agent Deep Reinforcement Learning for Point-Feature Label Placement—0
Reinforced Latent Reasoning for LLM-based Recommendation—0
Reinforced MLLM: A Survey on RL-Based Reasoning in Multimodal Large Language Models—0
Reinforced MOOCs Concept Recommendation in Heterogeneous Information Networks—0
Reinforced Multi-task Approach for Multi-hop Question Generation—0
Reinforced Pedestrian Attribute Recognition with Group Optimization Reward—0
Reinforced Self-Training (ReST) for Language Modeling—0
Reinforced Training Data Selection for Domain Adaptation—0
Reinforced UI Instruction Grounding: Towards a Generic UI Task Automation API—0
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Benchmark Results

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