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 10101–10150 of 15113 papers

TitleStatusHype
Ergodic Annealing—0
Deep Reinforcement Learning using Cyclical Learning Rates—0
IntelligentPooling: Practical Thompson Sampling for mHealth—0
Queueing Network Controls via Deep Reinforcement LearningCode0
Chance Constrained Policy Optimization for Process Control and Optimization—0
PixL2R: Guiding Reinforcement Learning Using Natural Language by Mapping Pixels to RewardsCode1
Moody Learners -- Explaining Competitive Behaviour of Reinforcement Learning Agents—0
MAPPER: Multi-Agent Path Planning with Evolutionary Reinforcement Learning in Mixed Dynamic Environments—0
Quantity vs. Quality: On Hyperparameter Optimization for Deep Reinforcement Learning—0
Compare and Select: Video Summarization with Multi-Agent Reinforcement Learning—0
Dreaming: Model-based Reinforcement Learning by Latent Imagination without Reconstruction—0
Multi-Step Reinforcement Learning for Single Image Super-ResolutionCode1
Hierarchical Control of Multi-Agent Systems using Online Reinforcement Learning—0
Deep Reinforcement Learning for Dynamic Spectrum Sensing and Aggregation in Multi-Channel Wireless Networks—0
Cooperative Internet of UAVs: Distributed Trajectory Design by Multi-agent Deep Reinforcement Learning—0
Munchausen Reinforcement LearningCode1
Lifelong Incremental Reinforcement Learning with Online Bayesian InferenceCode1
Intelligent Trajectory Planning in UAV-mounted Wireless Networks: A Quantum-Inspired Reinforcement Learning Perspective—0
Greedy Bandits with Sampled Context—0
Off-policy Evaluation in Infinite-Horizon Reinforcement Learning with Latent Confounders—0
Point Cloud Based Reinforcement Learning for Sim-to-Real and Partial Observability in Visual Navigation—0
Noisy Agents: Self-supervised Exploration by Predicting Auditory Events—0
Adaptive Height Optimisation for Cellular-Connected UAVs using Reinforcement Learning—0
Fast active learning for pure exploration in reinforcement learning—0
FlexPool: A Distributed Model-Free Deep Reinforcement Learning Algorithm for Joint Passengers & Goods Transportation—0
Combining Deep Reinforcement Learning and Search for Imperfect-Information GamesCode1
Data-efficient visuomotor policy training using reinforcement learning and generative models—0
Automated Discovery of Local Rules for Desired Collective-Level Behavior Through Reinforcement LearningCode0
Automated Database Indexing using Model-free Reinforcement Learning—0
Weak Human Preference Supervision For Deep Reinforcement LearningCode0
Variance Reduction for Deep Q-Learning using Stochastic Recursive Gradient—0
Safe Model-Based Reinforcement Learning for Systems with Parametric Uncertainties—0
Integrated Longitudinal Speed Decision-Making and Energy Efficiency Control for Connected Electrified Vehicles—0
Deep Inverse Reinforcement Learning for Structural Evolution of Small MoleculesCode0
A Comparative Study of AI-based Intrusion Detection Techniques in Critical Infrastructures—0
BabyAI 1.1Code1
Maximum Mutation Reinforcement Learning for Scalable ControlCode1
Bayesian Robust Optimization for Imitation LearningCode0
Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued PoliciesCode1
Autonomous Exploration Under Uncertainty via Deep Reinforcement Learning on GraphsCode1
Adaptive Energy Management for Real Driving Conditions via Transfer Reinforcement Learning—0
Distributional Reinforcement Learning via Moment MatchingCode1
Value-Decomposition Multi-Agent Actor-CriticsCode1
Monte-Carlo Tree Search as Regularized Policy OptimizationCode1
Bridging the Imitation Gap by Adaptive Insubordination—0
Reinforcement Learning with Fast Stabilization in Linear Dynamical Systems—0
Challenging common bolus advisor for self-monitoring type-I diabetes patients using Reinforcement LearningCode0
Integrating Deep Reinforcement Learning Networks with Health System SimulationsCode1
Adaptive Traffic Control with Deep Reinforcement Learning:Towards State-of-the-art and BeyondCode0
Buffer Pool Aware Query Scheduling via Deep Reinforcement Learning—0
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Benchmark Results

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