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 2251–2275 of 15113 papers

TitleStatusHype
Reinforcement Learning in Agent-Based Market Simulation: Unveiling Realistic Stylized Facts and Behavior—0
Offline Imitation Learning from Multiple Baselines with Applications to Compiler Optimization—0
Robustness and Visual Explanation for Black Box Image, Video, and ECG Signal Classification with Reinforcement Learning—0
LORD: Large Models based Opposite Reward Design for Autonomous Driving—0
From Two-Dimensional to Three-Dimensional Environment with Q-Learning: Modeling Autonomous Navigation with Reinforcement Learning and no LibrariesCode0
Image Deraining via Self-supervised Reinforcement Learning—0
CaT: Constraints as Terminations for Legged Locomotion Reinforcement Learning—0
Towards Human-Centered Construction Robotics: A Reinforcement Learning-Driven Companion Robot for Contextually Assisting Carpentry Workers—0
Long and Short-Term Constraints Driven Safe Reinforcement Learning for Autonomous Driving—0
Safe and Robust Reinforcement Learning: Principles and Practice—0
FPGA-Based Neural Thrust Controller for UAVs—0
Probabilistic Model Checking of Stochastic Reinforcement Learning Policies—0
PeersimGym: An Environment for Solving the Task Offloading Problem with Reinforcement LearningCode1
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
Reinforcement Learning-based Receding Horizon Control using Adaptive Control Barrier Functions for Safety-Critical SystemsCode1
TractOracle: towards an anatomically-informed reward function for RL-based tractographyCode1
RL for Consistency Models: Faster Reward Guided Text-to-Image Generation—0
Policy Optimization finds Nash Equilibrium in Regularized General-Sum LQ Games—0
Semantic-Aware Remote Estimation of Multiple Markov Sources Under Constraints—0
Reinforcement Learning-based Recommender Systems with Large Language Models for State Reward and Action Modeling—0
Outcome-Constrained Large Language Models for Countering Hate Speech—0
Planning with a Learned Policy Basis to Optimally Solve Complex Tasks—0
Task-optimal data-driven surrogate models for eNMPC via differentiable simulation and optimization—0
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Benchmark Results

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