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 3011–3020 of 15113 papers

TitleStatusHype
Transforming Multimodal Models into Action Models for Radiotherapy—0
LLM Alignment as Retriever Optimization: An Information Retrieval Perspective—0
Reinforcement Learning Based Prediction of PID Controller Gains for Quadrotor UAVs—0
Optimizing Electric Vehicles Charging using Large Language Models and Graph Neural Networks—0
AI-driven materials design: a mini-review—0
Calibrated Unsupervised Anomaly Detection in Multivariate Time-series using Reinforcement Learning—0
Underwater Soft Fin Flapping Motion with Deep Neural Network Based Surrogate ModelCode0
OmniRL: In-Context Reinforcement Learning by Large-Scale Meta-Training in Randomized Worlds—0
Adviser-Actor-Critic: Eliminating Steady-State Error in Reinforcement Learning Control—0
Circular Microalgae-Based Carbon Control for Net ZeroCode0
Show:102550
← PrevPage 302 of 1512Next →

Benchmark Results

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