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 5726–5750 of 15113 papers

TitleStatusHype
STL-Based Synthesis of Feedback Controllers Using Reinforcement LearningCode0
Selecting Mechanical Parameters of a Monopode Jumping System with Reinforcement Learning—0
Fuse and Adapt: Investigating the Use of Pre-Trained Self-Supervising Learning Models in Limited Data NLU problems—0
CT-DQN: Control-Tutored Deep Reinforcement Learning—0
Flow to Control: Offline Reinforcement Learning with Lossless Primitive Discovery—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
Launchpad: Learning to Schedule Using Offline and Online RL Methods—0
Kick-motion Training with DQN in AI Soccer Environment—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
Modeling Mobile Health Users as Reinforcement Learning Agents—0
Reward Function Optimization of a Deep Reinforcement Learning Collision Avoidance System—0
Policy Optimization over General State and Action Spaces—0
Targets in Reinforcement Learning to solve Stackelberg Security Games—0
Reinforcement Learning for Multi-Truck Vehicle Routing Problems—0
Safe and Efficient Reinforcement Learning Using Disturbance-Observer-Based Control Barrier Functions—0
Random Copolymer inverse design system orienting on Accurate discovering of Antimicrobial peptide-mimetic copolymers—0
KRLS: Improving End-to-End Response Generation in Task Oriented Dialog with Reinforced Keywords LearningCode0
Funnel-based Reward Shaping for Signal Temporal Logic Tasks in Reinforcement Learning—0
Welfare and Fairness in Multi-objective Reinforcement LearningCode0
General policy mapping: online continual reinforcement learning inspired on the insect brainCode0
Computationally Efficient Reinforcement Learning: Targeted Exploration leveraging Simple Rules—0
Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning—0
Automatic Discovery of Multi-perspective Process Model using Reinforcement Learning—0
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Benchmark Results

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