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 5701–5725 of 15113 papers

TitleStatusHype
Interpretable Stochastic Model Predictive Control using Distributional Reinforced Estimation for Quadrotor Tracking Systems—0
Cliff Diving: Exploring Reward Surfaces in Reinforcement Learning Environments—0
Unified Distributed EnvironmentCode0
Efficient Off-Policy Reinforcement Learning via Brain-Inspired Computing—0
Deep Reinforcement Learning in mmW-NOMA: Joint Power Allocation and Hybrid Beamforming—0
Deep Reinforcement Learning for Computational Fluid Dynamics on HPC SystemsCode1
Distributed Transmission Control for Wireless Networks using Multi-Agent Reinforcement LearningCode0
Joint Power Allocation and Beamformer for mmW-NOMA Downlink Systems by Deep Reinforcement Learning—0
Emergent Bartering Behaviour in Multi-Agent Reinforcement Learning—0
Towards Understanding the Link Between Modularity and Performance in Neural Networks for Reinforcement LearningCode0
Upside-Down Reinforcement Learning Can Diverge in Stochastic Environments With Episodic ResetsCode0
Provably Safe Reinforcement Learning: Conceptual Analysis, Survey, and Benchmarking—0
Feature and Instance Joint Selection: A Reinforcement Learning Perspective—0
Provably Safe Deep Reinforcement Learning for Robotic Manipulation in Human Environments—0
Contingency-constrained economic dispatch with safe reinforcement learning—0
Economical Precise Manipulation and Auto Eye-Hand Coordination with Binocular Visual Reinforcement Learning—0
Accounting for the Sequential Nature of States to Learn Features for Reinforcement Learning—0
Controlling chaotic itinerancy in laser dynamics for reinforcement learning—0
Bridging Model-based Safety and Model-free Reinforcement Learning through System Identification of Low Dimensional Linear Models—0
Delayed Reinforcement Learning by Imitation—0
Intelligent Reflecting Surface Configurations for Smart Radio Using Deep Reinforcement LearningCode1
Efficient Distributed Framework for Collaborative Multi-Agent Reinforcement Learning—0
Final Iteration Convergence Bound of Q-Learning: Switching System Approach—0
A State-Distribution Matching Approach to Non-Episodic Reinforcement LearningCode0
Learning to Guide Multiple Heterogeneous Actors from a Single Human Demonstration via Automatic Curriculum Learning in StarCraft II—0
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Benchmark Results

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