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 2851–2860 of 15113 papers

TitleStatusHype
AbstRaL: Augmenting LLMs' Reasoning by Reinforcing Abstract Thinking—0
Augmenting Control over Exploration Space in Molecular Dynamics Simulators to Streamline De Novo Analysis through Generative Control Policies—0
AceReason-Nemotron 1.1: Advancing Math and Code Reasoning through SFT and RL Synergy—0
Augmenting Automated Game Testing with Deep Reinforcement Learning—0
Augmented Replay Memory in Reinforcement Learning With Continuous Control—0
Data-Efficient Pipeline for Offline Reinforcement Learning with Limited Data—0
Data-Efficient Quadratic Q-Learning Using LMIs—0
Augmented Random Search for Quadcopter Control: An alternative to Reinforcement Learning—0
Combining Multi-Objective Bayesian Optimization with Reinforcement Learning for TinyML—0
AITuning: Machine Learning-based Tuning Tool for Run-Time Communication Libraries—0
Show:102550
← PrevPage 286 of 1512Next →

Benchmark Results

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