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 4251–4275 of 15113 papers

TitleStatusHype
Random Copolymer inverse design system orienting on Accurate discovering of Antimicrobial peptide-mimetic copolymers—0
Policy Optimization over General State and Action Spaces—0
KRLS: Improving End-to-End Response Generation in Task Oriented Dialog with Reinforced Keywords LearningCode0
Reinforcement Learning for Multi-Truck Vehicle Routing Problems—0
Safe and Efficient Reinforcement Learning Using Disturbance-Observer-Based Control Barrier Functions—0
Targets in Reinforcement Learning to solve Stackelberg Security Games—0
Computationally Efficient Reinforcement Learning: Targeted Exploration leveraging Simple Rules—0
Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning—0
Efficient Reinforcement Learning Through Trajectory GenerationCode1
General policy mapping: online continual reinforcement learning inspired on the insect brainCode0
ConvLab-3: A Flexible Dialogue System Toolkit Based on a Unified Data FormatCode1
Automatic Discovery of Multi-perspective Process Model using Reinforcement Learning—0
Distributed Energy Management and Demand Response in Smart Grids: A Multi-Agent Deep Reinforcement Learning Framework—0
Behavior Estimation from Multi-Source Data for Offline Reinforcement LearningCode0
Autotuning PID control using Actor-Critic Deep Reinforcement Learning—0
Approximating Martingale Process for Variance Reduction in Deep Reinforcement Learning with Large State Space—0
Learning and Understanding a Disentangled Feature Representation for Hidden Parameters in Reinforcement Learning—0
Offline Policy Evaluation and Optimization under Confounding—0
Symmetry Detection in Trajectory Data for More Meaningful Reinforcement Learning Representations—0
Multi-Agent Reinforcement Learning for Microprocessor Design Space Exploration—0
Offline Reinforcement Learning with Closed-Form Policy Improvement Operators—0
The Effectiveness of World Models for Continual Reinforcement LearningCode1
Discrete Control in Real-World Driving Environments using Deep Reinforcement Learning—0
Beyond CAGE: Investigating Generalization of Learned Autonomous Network Defense Policies—0
Causal Deep Reinforcement Learning Using Observational Data—0
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Benchmark Results

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