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 4226–4250 of 15113 papers

TitleStatusHype
Reinforcement learning with Demonstrations from Mismatched Task under Sparse Reward—0
DACOM: Learning Delay-Aware Communication for Multi-Agent Reinforcement Learning—0
Constrained Reinforcement Learning via Dissipative Saddle Flow Dynamics—0
MeshDQN: A Deep Reinforcement Learning Framework for Improving Meshes in Computational Fluid DynamicsCode1
STL-Based Synthesis of Feedback Controllers Using Reinforcement LearningCode0
On the Energy and Communication Efficiency Tradeoffs in Federated and Multi-Task Learning—0
Utilizing Prior Solutions for Reward Shaping and Composition in Entropy-Regularized Reinforcement Learning—0
Selecting Mechanical Parameters of a Monopode Jumping System with Reinforcement Learning—0
CT-DQN: Control-Tutored Deep Reinforcement Learning—0
Flow to Control: Offline Reinforcement Learning with Lossless Primitive Discovery—0
Fuse and Adapt: Investigating the Use of Pre-Trained Self-Supervising Learning Models in Limited Data NLU problems—0
Launchpad: Learning to Schedule Using Offline and Online RL Methods—0
Karolos: An Open-Source Reinforcement Learning Framework for Robot-Task EnvironmentsCode1
Reward Function Optimization of a Deep Reinforcement Learning Collision Avoidance System—0
Modeling Mobile Health Users as Reinforcement Learning Agents—0
Online Learning-based Waveform Selection for Improved Vehicle Recognition in Automotive Radar—0
Safe Reinforcement Learning with Probabilistic Control Barrier Functions for Ramp Merging—0
Distributed Deep Reinforcement Learning: A Survey and A Multi-Player Multi-Agent Learning Toolbox—0
Five Properties of Specific Curiosity You Didn't Know Curious Machines Should Have—0
Kick-motion Training with DQN in AI Soccer Environment—0
One Risk to Rule Them All: A Risk-Sensitive Perspective on Model-Based Offline Reinforcement LearningCode1
MO-Gym: A Library of Multi-Objective Reinforcement Learning EnvironmentsCode2
Real-time Bidding Strategy in Display Advertising: An Empirical AnalysisCode1
Welfare and Fairness in Multi-objective Reinforcement LearningCode0
Funnel-based Reward Shaping for Signal Temporal Logic Tasks in Reinforcement Learning—0
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Benchmark Results

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