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 2811–2820 of 15113 papers

TitleStatusHype
ViVa: Video-Trained Value Functions for Guiding Online RL from Diverse Data—0
Transferable Latent-to-Latent Locomotion Policy for Efficient and Versatile Motion Control of Diverse Legged Robots—0
A Roadmap Towards Improving Multi-Agent Reinforcement Learning With Causal Discovery And Inference—0
ComfyGPT: A Self-Optimizing Multi-Agent System for Comprehensive ComfyUI Workflow Generation—0
Curriculum RL meets Monte Carlo Planning: Optimization of a Real World Container Management ProblemCode0
Autonomous Radiotherapy Treatment Planning Using DOLA: A Privacy-Preserving, LLM-Based Optimization Agent—0
Causally Aligned Curriculum Learning—0
OThink-MR1: Stimulating multimodal generalized reasoning capabilities via dynamic reinforcement learning—0
UAS Visual Navigation in Large and Unseen Environments via a Meta Agent—0
Reinforcement Learning-based Heuristics to Guide Domain-Independent Dynamic ProgrammingCode0
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Benchmark Results

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