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 6926–6950 of 15113 papers

TitleStatusHype
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
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
Provably Safe Deep Reinforcement Learning for Robotic Manipulation in Human Environments—0
Contingency-constrained economic dispatch with safe reinforcement learning—0
Feature and Instance Joint Selection: A Reinforcement Learning Perspective—0
Economical Precise Manipulation and Auto Eye-Hand Coordination with Binocular Visual Reinforcement Learning—0
Controlling chaotic itinerancy in laser dynamics for reinforcement learning—0
Accounting for the Sequential Nature of States to Learn Features for Reinforcement Learning—0
Characterizing the Action-Generalization Gap in Deep Q-Learning—0
Efficient Distributed Framework for Collaborative Multi-Agent Reinforcement Learning—0
Delayed Reinforcement Learning by Imitation—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
Developing cooperative policies for multi-stage reinforcement learning tasks—0
Bridging Model-based Safety and Model-free Reinforcement Learning through System Identification of Low Dimensional Linear Models—0
Final Iteration Convergence Bound of Q-Learning: Switching System Approach—0
Hybrid Reinforcement Learning for STAR-RISs: A Coupled Phase-Shift Model Based Beamformer—0
On the Verge of Solving Rocket League using Deep Reinforcement Learning and Sim-to-sim Transfer—0
Accelerated Reinforcement Learning for Temporal Logic Control Objectives—0
Introduction to Soar—0
Simultaneous Double Q-learning with Conservative Advantage Learning for Actor-Critic MethodsCode0
Search-Based Testing of Reinforcement Learning—0
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Benchmark Results

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