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 3651–3700 of 15113 papers

TitleStatusHype
Learning to Influence Human Behavior with Offline Reinforcement Learning—0
Hindsight States: Blending Sim and Real Task Elements for Efficient Reinforcement Learning—0
RePreM: Representation Pre-training with Masked Model for Reinforcement Learning—0
CoRL: Environment Creation and Management Focused on System IntegrationCode1
Toward Risk-based Optimistic Exploration for Cooperative Multi-Agent Reinforcement Learning—0
Intelligent O-RAN Traffic Steering for URLLC Through Deep Reinforcement Learning—0
Guarded Policy Optimization with Imperfect Online Demonstrations—0
Tile Networks: Learning Optimal Geometric Layout for Whole-page Recommendation—0
POPGym: Benchmarking Partially Observable Reinforcement LearningCode2
Approximating Energy Market Clearing and Bidding With Model-Based Reinforcement Learning—0
T-Cell Receptor Optimization with Reinforcement Learning and Mutation Policies for Precesion Immunotherapy—0
Multi-Start Team Orienteering Problem for UAS Mission Re-Planning with Data-Efficient Deep Reinforcement Learning—0
Data-efficient, Explainable and Safe Box Manipulation: Illustrating the Advantages of Physical Priors in Model-Predictive Control—0
Resource-Constrained Station-Keeping for Helium Balloons using Reinforcement Learning—0
Preference Transformer: Modeling Human Preferences using Transformers for RLCode1
Domain Adaptation of Reinforcement Learning Agents based on Network Service Proximity—0
Expert-Free Online Transfer Learning in Multi-Agent Reinforcement Learning—0
The Ladder in Chaos: A Simple and Effective Improvement to General DRL Algorithms by Policy Path Trimming and Boosting—0
Self-Improving Robots: End-to-End Autonomous Visuomotor Reinforcement Learning—0
Parameter Sharing with Network Pruning for Scalable Multi-Agent Deep Reinforcement Learning—0
Compensating for Sensing Failures via Delegation in Human-AI Hybrid Systems—0
Co-learning Planning and Control Policies Constrained by Differentiable Logic Specifications—0
Reinforcement Learning Guided Multi-Objective Exam Paper GenerationCode0
GHQ: Grouped Hybrid Q Learning for Heterogeneous Cooperative Multi-agent Reinforcement LearningCode0
Reinforced Labels: Multi-Agent Deep Reinforcement Learning for Point-Feature Label Placement—0
A Deep Reinforcement Learning Trader without Offline Training—0
A Variational Approach to Mutual Information-Based Coordination for Multi-Agent Reinforcement Learning—0
LS-IQ: Implicit Reward Regularization for Inverse Reinforcement LearningCode1
Human-Inspired Framework to Accelerate Reinforcement LearningCode0
Parameter Optimization of LLC-Converter with multiple operation points using Reinforcement Learning—0
Learning to Control Autonomous Fleets from Observation via Offline Reinforcement LearningCode0
Efficient Exploration Using Extra Safety Budget in Constrained Policy Optimization—0
Auxiliary Task-based Deep Reinforcement Learning for Quantum Control—0
Multi-Agent Reinforcement Learning for Pragmatic Communication and Control—0
Graph Reinforcement Learning for Operator Selection in the ALNS Metaheuristic—0
Minimizing the Outage Probability in a Markov Decision Process—0
Exploiting Multiple Abstractions in Episodic RL via Reward ShapingCode0
AR3n: A Reinforcement Learning-based Assist-As-Needed Controller for Robotic Rehabilitation—0
Hierarchical Reinforcement Learning in Complex 3D Environments—0
The In-Sample Softmax for Offline Reinforcement LearningCode1
Distributional Method for Risk Averse Reinforcement Learning—0
Exposure-Based Multi-Agent Inspection of a Tumbling Target Using Deep Reinforcement Learning—0
Reward Design with Language ModelsCode2
Reinforcement Learning with Depreciating Assets—0
The Provable Benefits of Unsupervised Data Sharing for Offline Reinforcement Learning—0
A Reinforcement Learning Approach for Scheduling Problems With Improved Generalization Through Order Swapping—0
Systematic Rectification of Language Models via Dead-end AnalysisCode0
Dynamic Resource Allocation for Metaverse Applications with Deep Reinforcement Learning—0
Sim-and-Real Reinforcement Learning for Manipulation: A Consensus-based Approach—0
Revolutionizing Genomics with Reinforcement Learning Techniques—0
Show:102550
← PrevPage 74 of 303Next →

Benchmark Results

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