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 11451–11475 of 15113 papers

TitleStatusHype
Algorithms in Multi-Agent Systems: A Holistic Perspective from Reinforcement Learning and Game Theory—0
MIME: Mutual Information Minimisation Exploration—0
Reward Shaping for Reinforcement Learning with Omega-Regular Objectives—0
Model-based Multi-Agent Reinforcement Learning with Cooperative Prioritized Sweeping—0
SEERL: Sample Efficient Ensemble Reinforcement Learning—0
Robotic Grasp Manipulation Using Evolutionary Computing and Deep Reinforcement Learning—0
Continuous-action Reinforcement Learning for Playing Racing Games: Comparing SPG to PPOCode0
Exploiting Language Instructions for Interpretable and Compositional Reinforcement Learning—0
Learning to Locomote with Deep Neural-Network and CPG-based Control in a Soft Snake Robot—0
Multi-Robot Formation Control Using Reinforcement Learning—0
Statistical Inference of the Value Function for Reinforcement Learning in Infinite Horizon SettingsCode0
Weakly Supervised Video Summarization by Hierarchical Reinforcement Learning—0
Deep Reinforcement Learning for Complex Manipulation Tasks with Sparse Feedback—0
Sparse Black-box Video Attack with Reinforcement LearningCode0
Reward Engineering for Object Pick and Place TrainingCode0
Deep Interactive Reinforcement Learning for Path Following of Autonomous Underwater Vehicle—0
A storage expansion planning framework using reinforcement learning and simulation-based optimization—0
Reinforcement Learning Tracking Control for Robotic Manipulator With Kernel-Based Dynamic Model—0
On Computation and Generalization of Generative Adversarial Imitation Learning—0
Perception and Navigation in Autonomous Systems in the Era of Learning: A Survey—0
Sample-based Distributional Policy Gradient—0
Multi-Agent Deep Reinforcement Learning for Cooperative Connected Vehicles—0
On Thompson Sampling for Smoother-than-Lipschitz Bandits—0
A Nonparametric Off-Policy Policy GradientCode0
EEG-based Drowsiness Estimation for Driving Safety using Deep Q-Learning—0
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Benchmark Results

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