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

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Showing 2261–2270 of 15113 papers

TitleStatusHype
From Two-Dimensional to Three-Dimensional Environment with Q-Learning: Modeling Autonomous Navigation with Reinforcement Learning and no LibrariesCode0
Probabilistic Model Checking of Stochastic Reinforcement Learning Policies—0
PeersimGym: An Environment for Solving the Task Offloading Problem with Reinforcement LearningCode1
TractOracle: towards an anatomically-informed reward function for RL-based tractographyCode1
Reinforcement Learning-based Receding Horizon Control using Adaptive Control Barrier Functions for Safety-Critical SystemsCode1
Learning the Optimal Power Flow: Environment Design MattersCode0
Depending on yourself when you should: Mentoring LLM with RL agents to become the master in cybersecurity games—0
Uncertainty-aware Distributional Offline Reinforcement Learning—0
RL for Consistency Models: Faster Reward Guided Text-to-Image Generation—0
Policy Optimization finds Nash Equilibrium in Regularized General-Sum LQ Games—0
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Benchmark Results

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