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 2071–2080 of 15113 papers

TitleStatusHype
HighwayLLM: Decision-Making and Navigation in Highway Driving with RL-Informed Language Model—0
Large Language Models (LLMs) Assisted Wireless Network Deployment in Urban Settings—0
Knowledge Graph Reasoning with Self-supervised Reinforcement LearningCode1
Multi-Agent Reinforcement Learning with Hierarchical Coordination for Emergency Responder Stationing—0
Practical and efficient quantum circuit synthesis and transpiling with Reinforcement Learning—0
CausalPlayground: Addressing Data-Generation Requirements in Cutting-Edge Causality ResearchCode1
A Multimodal Learning-based Approach for Autonomous Landing of UAV—0
Rethinking Robustness Assessment: Adversarial Attacks on Learning-based Quadrupedal Locomotion Controllers—0
Investigating the Impact of Choice on Deep Reinforcement Learning for Space Controls—0
Learning Future Representation with Synthetic Observations for Sample-efficient Reinforcement Learning—0
Show:102550
← PrevPage 208 of 1512Next →

Benchmark Results

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