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 10451–10500 of 15113 papers

TitleStatusHype
SAMBA: Safe Model-Based & Active Reinforcement LearningCode1
Safety-guaranteed Reinforcement Learning based on Multi-class Support Vector Machine—0
Continuous Control for Searching and Planning with a Learned Model—0
Deep Reinforcement Learning for Neural Control—0
A Brief Look at Generalization in Visual Meta-Reinforcement Learning—0
Human and Multi-Agent collaboration in a human-MARL teaming framework—0
Decorrelated Double Q-learning—0
Shared Experience Actor-Critic for Multi-Agent Reinforcement LearningCode1
Potential Field Guided Actor-Critic Reinforcement Learning—0
Mutual Information Based Knowledge Transfer Under State-Action Dimension MismatchCode0
TorsionNet: A Reinforcement Learning Approach to Sequential Conformer SearchCode1
Meta-Reinforcement Learning Robust to Distributional Shift via Model Identification and Experience Relabeling—0
Recurrent Sum-Product-Max Networks for Decision Making in Perfectly-Observed EnvironmentsCode0
Using Reinforcement Learning to Allocate and Manage Service Function Chains in Cellular Networks—0
Modelling Hierarchical Structure between Dialogue Policy and Natural Language Generator with Option Framework for Task-oriented Dialogue SystemCode1
Surveys without Questions: A Reinforcement Learning Approach—0
Sample Efficient Reinforcement Learning via Low-Rank Matrix Estimation—0
Multi-Agent Informational Learning Processes—0
Exploration by Maximizing Rényi Entropy for Reward-Free RL Framework—0
Deep Reinforcement Learning for Electric Transmission Voltage Control—0
Closed Loop Neural-Symbolic Learning via Integrating Neural Perception, Grammar Parsing, and Symbolic ReasoningCode1
Multi-Agent Reinforcement Learning in Stochastic Networked SystemsCode0
Scalable Multi-Agent Reinforcement Learning for Networked Systems with Average Reward—0
Zeroth-Order Supervised Policy Improvement—0
Multi-Agent Reinforcement Learning in a Realistic Limit Order Book Market Simulation—0
Off-Policy Risk-Sensitive Reinforcement Learning Based Constrained Robust Optimal Control—0
Q-greedyUCB: a New Exploration Policy for Adaptive and Resource-efficient Scheduling—0
Privacy-Cost Management in Smart Meters with Mutual Information-Based Reinforcement Learning—0
Deep reinforcement learning for optical systems: A case study of mode-locked lasers—0
Learning to Incentivize Other Learning AgentsCode1
Robust Spammer Detection by Nash Reinforcement LearningCode1
Self-Supervised Reinforcement Learning for Recommender Systems—0
Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation—0
Continuous Action Reinforcement Learning from a Mixture of Interpretable ExpertsCode0
What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical StudyCode1
Machine learning and control engineering: The model-free case—0
Transient Non-Stationarity and Generalisation in Deep Reinforcement Learning—0
Learning to Play Table Tennis From Scratch using Muscular Robots—0
Development of A Stochastic Traffic Environment with Generative Time-Series Models for Improving Generalization Capabilities of Autonomous Driving Agents—0
An overall view of key problems in algorithmic trading and recent progress—0
Online Learning in Iterated Prisoner's Dilemma to Mimic Human BehaviorCode0
Stealing Deep Reinforcement Learning Models for Fun and Profit—0
Policy-focused Agent-based Modeling using RL Behavioral Models—0
Variational Model-based Policy Optimization—0
Distributed Learning on Heterogeneous Resource-Constrained Devices—0
Constrained episodic reinforcement learning in concave-convex and knapsack settingsCode1
Causal Discovery from Incomplete Data using An Encoder and Reinforcement Learning—0
Constrained Upper Confidence Reinforcement Learning with Known Dynamics—0
Learning the model-free linear quadratic regulator via random search—0
Learning to Plan via Deep Optimistic Value Exploration—0
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Benchmark Results

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