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 4601–4650 of 15113 papers

TitleStatusHype
Safe Model-Based Reinforcement Learning with an Uncertainty-Aware Reachability CertificateCode1
Adaptive patch foraging in deep reinforcement learning agents—0
Query Rewriting for Effective Misinformation Discovery—0
Abstract-to-Executable Trajectory Translation for One-Shot Task GeneralizationCode1
Distributional Reward Estimation for Effective Multi-Agent Deep Reinforcement LearningCode0
A Reinforcement Learning Approach to Estimating Long-term Treatment Effects—0
Frame Mining: a Free Lunch for Learning Robotic Manipulation from 3D Point CloudsCode1
A Scalable Finite Difference Method for Deep Reinforcement Learning—0
Just Round: Quantized Observation Spaces Enable Memory Efficient Learning of Dynamic LocomotionCode0
Deep reinforcement learning for automatic run-time adaptation of UWB PHY radio settings—0
A Concise Introduction to Reinforcement Learning in Robotics—0
Harfang3D Dog-Fight Sandbox: A Reinforcement Learning Research Platform for the Customized Control Tasks of Fighter AircraftsCode2
Bootstrap Advantage Estimation for Policy Optimization in Reinforcement LearningCode0
Reinforcement Learning with Unbiased Policy Evaluation and Linear Function Approximation—0
Object-Category Aware Reinforcement Learning—0
Visual Reinforcement Learning with Self-Supervised 3D RepresentationsCode1
Personalized Federated Hypernetworks for Privacy Preservation in Multi-Task Reinforcement Learning—0
Towards Multi-Agent Reinforcement Learning driven Over-The-Counter Market Simulations—0
Policy Gradient With Serial Markov Chain Reasoning—0
Optimal Control of Material Micro-Structures—0
Multi-agent Dynamic Algorithm ConfigurationCode1
Sustainable Online Reinforcement Learning for Auto-biddingCode1
Towards Trustworthy Automatic Diagnosis Systems by Emulating Doctors' Reasoning with Deep Reinforcement LearningCode1
Observed Adversaries in Deep Reinforcement Learning—0
Output Feedback Adaptive Optimal Control of Affine Nonlinear systems with a Linear Measurement Model—0
Model-Based Offline Reinforcement Learning with Pessimism-Modulated Dynamics BeliefCode0
A Mixture of Surprises for Unsupervised Reinforcement LearningCode1
Efficient circuit implementation for coined quantum walks on binary trees and application to reinforcement learning—0
Dissipative residual layers for unsupervised implicit parameterization of data manifolds—0
Causality-driven Hierarchical Structure Discovery for Reinforcement Learning—0
Contrastive Retrospection: honing in on critical steps for rapid learning and generalization in RLCode1
Real World Offline Reinforcement Learning with Realistic Data Source—0
Semi-Supervised Offline Reinforcement Learning with Action-Free TrajectoriesCode1
Reinforcement Learning with Automated Auxiliary Loss Search—0
Smooth Trajectory Collision Avoidance through Deep Reinforcement Learning—0
DQLAP: Deep Q-Learning Recommender Algorithm with Update Policy for a Real Steam Turbine System—0
Efficient Adversarial Training without Attacking: Worst-Case-Aware Robust Reinforcement LearningCode1
A Unified Framework for Alternating Offline Model Training and Policy LearningCode0
Explaining Online Reinforcement Learning Decisions of Self-Adaptive Systems—0
Centralized Training with Hybrid Execution in Multi-Agent Reinforcement LearningCode0
MARLlib: A Scalable and Efficient Multi-agent Reinforcement Learning LibraryCode3
The Role of Exploration for Task Transfer in Reinforcement Learning—0
Exploration via Elliptical Episodic BonusesCode1
Discovered Policy OptimisationCode3
Edge-Cloud Cooperation for DNN Inference via Reinforcement Learning and Supervised Learning—0
Broad-persistent Advice for Interactive Reinforcement Learning Scenarios—0
DHRL: A Graph-Based Approach for Long-Horizon and Sparse Hierarchical Reinforcement LearningCode1
Reliable Conditioning of Behavioral Cloning for Offline Reinforcement LearningCode1
Multi-User Reinforcement Learning with Low Rank Rewards—0
Mastering the Game of No-Press Diplomacy via Human-Regularized Reinforcement Learning and PlanningCode3
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Benchmark Results

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