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 11051–11100 of 15113 papers

TitleStatusHype
Near-optimal Regret Bounds for Stochastic Shortest Path—0
Rapidly Personalizing Mobile Health Treatment Policies with Limited Data—0
Optimizing Traffic Lights with Multi-agent Deep Reinforcement Learning and V2X communication—0
Discriminative Particle Filter Reinforcement Learning for Complex Partial ObservationsCode1
Deep Reinforcement Learning with Linear Quadratic Regulator Regions—0
Adversarial Radar Inference. From Inverse Tracking to Inverse Reinforcement Learning of Cognitive Radar—0
Automatic Data Augmentation via Deep Reinforcement Learning for Effective Kidney Tumor Segmentation—0
Guided Constrained Policy Optimization for Dynamic Quadrupedal Robot Locomotion—0
Vehicle Tracking in Wireless Sensor Networks via Deep Reinforcement Learning—0
Reinforcement Learning Framework for Deep Brain Stimulation StudyCode1
Data Freshness and Energy-Efficient UAV Navigation Optimization: A Deep Reinforcement Learning Approach—0
On the Search for Feedback in Reinforcement Learning—0
Disentangling Controllable Object through Video Prediction Improves Visual Reinforcement Learning—0
Accelerating Reinforcement Learning with a Directional-Gaussian-Smoothing Evolution Strategy—0
Automatic Gesture Recognition in Robot-assisted Surgery with Reinforcement Learning and Tree Search—0
Enhanced Adversarial Strategically-Timed Attacks against Deep Reinforcement Learning—0
Adaptive Temporal Difference Learning with Linear Function Approximation—0
oIRL: Robust Adversarial Inverse Reinforcement Learning with Temporally Extended Actions—0
Multi-Agent Reinforcement Learning as a Computational Tool for Language Evolution Research: Historical Context and Future Challenges—0
Multi-Agent Meta-Reinforcement Learning for Self-Powered and Sustainable Edge Computing Systems—0
Debiased Off-Policy Evaluation for Recommendation Systems—0
UAV Aided Search and Rescue Operation Using Reinforcement Learning—0
Sim2Real Transfer for Reinforcement Learning without Dynamics RandomizationCode1
Value-driven Hindsight Modelling—0
Optimistic Policy Optimization with Bandit Feedback—0
Efficient Deep Reinforcement Learning via Adaptive Policy TransferCode0
Curriculum in Gradient-Based Meta-Reinforcement Learning—0
How To Avoid Being Eaten By a Grue: Exploration Strategies for Text-Adventure AgentsCode1
Keep Doing What Worked: Behavioral Modelling Priors for Offline Reinforcement Learning—0
Generating Automatic Curricula via Self-Supervised Active Domain RandomizationCode1
Empirical Policy Evaluation with Supergraphs—0
Adaptive Estimator Selection for Off-Policy EvaluationCode0
KoGuN: Accelerating Deep Reinforcement Learning via Integrating Human Suboptimal Knowledge—0
MoTiAC: Multi-Objective Actor-Critics for Real-Time Bidding—0
Reinforcement Learning for Molecular Design Guided by Quantum MechanicsCode1
Multi-Issue Bargaining With Deep Reinforcement Learning—0
Langevin DQNCode0
Kalman meets Bellman: Improving Policy Evaluation through Value TrackingCode1
Control Frequency Adaptation via Action Persistence in Batch Reinforcement LearningCode0
Adaptive Experience Selection for Policy Gradient—0
Reinforcement learning for the privacy preservation and manipulation of eye tracking data—0
Reward Design for Driver Repositioning Using Multi-Agent Reinforcement Learning—0
Reinforced active learning for image segmentationCode1
R-MADDPG for Partially Observable Environments and Limited CommunicationCode1
Investigating Simple Object Representations in Model-Free Deep Reinforcement Learning—0
The Archimedean trap: Why traditional reinforcement learning will probably not yield AGI—0
Non-asymptotic Convergence of Adam-type Reinforcement Learning Algorithms under Markovian Sampling—0
PDDLGym: Gym Environments from PDDL ProblemsCode1
Universal Value Density Estimation for Imitation Learning and Goal-Conditioned Reinforcement LearningCode0
Deep RL Agent for a Real-Time Action Strategy GameCode1
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Benchmark Results

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