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 3876–3900 of 15113 papers

TitleStatusHype
A General Theory of Relativity in Reinforcement Learning—0
Adaptive Road Configurations for Improved Autonomous Vehicle-Pedestrian Interactions using Reinforcement Learning—0
DeepThermal: Combustion Optimization for Thermal Power Generating Units Using Offline Reinforcement Learning—0
CORE: Constraint-Aware One-Step Reinforcement Learning for Simulation-Guided Neural Network Accelerator Design—0
Effective Reinforcement Learning Based on Structural Information Principles—0
Deep Transfer Q-Learning for Offline Non-Stationary Reinforcement Learning—0
A Comparative Study of Reinforcement Learning Techniques on Dialogue Management—0
CORAL: Contextual Response Retrievability Loss Function for Training Dialog Generation Models—0
A Multiagent Reinforcement Learning Algorithm with Non-linear Dynamics—0
ACTRCE: Augmenting Experience via Teacher’s Advice—0
DeepVideo-R1: Video Reinforcement Fine-Tuning via Difficulty-aware Regressive GRPO—0
Balancing a CartPole System with Reinforcement Learning -- A Tutorial—0
Deep VULMAN: A Deep Reinforcement Learning-Enabled Cyber Vulnerability Management Framework—0
DeepWiVe: Deep-Learning-Aided Wireless Video Transmission—0
Balancing Accuracy and Fairness for Interactive Recommendation with Reinforcement Learning—0
CoRAL: Collaborative Retrieval-Augmented Large Language Models Improve Long-tail Recommendation—0
Adversary Agnostic Robust Deep Reinforcement Learning—0
Balancing Constraints and Rewards with Meta-Gradient D4PG—0
Defense Against Reward Poisoning Attacks in Reinforcement Learning—0
Assessment of Reward Functions in Reinforcement Learning for Multi-Modal Urban Traffic Control under Real-World limitations—0
Defining Admissible Rewards for High Confidence Policy Evaluation—0
Definition and evaluation of model-free coordination of electrical vehicle charging with reinforcement learning—0
A General Perspective on Objectives of Reinforcement Learning—0
Deflated Dynamics Value Iteration—0
Distributed Reinforcement Learning for Robot Teams: A Review—0
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Benchmark Results

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