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 2661–2670 of 15113 papers

TitleStatusHype
Adaptive 3D UI Placement in Mixed Reality Using Deep Reinforcement Learning—0
Phi-4-Mini-Reasoning: Exploring the Limits of Small Reasoning Language Models in Math—0
Reinforced MLLM: A Survey on RL-Based Reasoning in Multimodal Large Language Models—0
PRISM: Projection-based Reward Integration for Scene-Aware Real-to-Sim-to-Real Transfer with Few Demonstrations—0
A Survey on GUI Agents with Foundation Models Enhanced by Reinforcement Learning—0
Token-Efficient RL for LLM Reasoning—0
AI Recommendation Systems for Lane-Changing Using Adherence-Aware Reinforcement Learning—0
Interactive Double Deep Q-network: Integrating Human Interventions and Evaluative Predictions in Reinforcement Learning of Autonomous Driving—0
Reinforcement Learning-Based Heterogeneous Multi-Task Optimization in Semantic Broadcast Communications—0
An Automated Reinforcement Learning Reward Design Framework with Large Language Model for Cooperative Platoon Coordination—0
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Benchmark Results

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