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 4401–4450 of 15113 papers

TitleStatusHype
Multiagent Copilot Approach for Shared Autonomy between Human EEG and TD3 Deep Reinforcement Learning—0
A Survey of Reinforcement Learning from Human Feedback—0
Optimizing Heat Alert Issuance with Reinforcement LearningCode0
Maximum entropy GFlowNets with soft Q-learning—0
Multi-Agent Probabilistic Ensembles with Trajectory Sampling for Connected Autonomous Vehicles—0
Parameterized Projected Bellman OperatorCode0
Optimal coordination of resources: A solution from reinforcement learning—0
Towards Machines that Trust: AI Agents Learn to Trust in the Trust Game—0
Neural Network Approximation for Pessimistic Offline Reinforcement Learning—0
Stable Relay Learning Optimization Approach for Fast Power System Production Cost Minimization Simulation—0
A Dual Curriculum Learning Framework for Multi-UAV Pursuit-Evasion in Diverse Environments—0
CUDC: A Curiosity-Driven Unsupervised Data Collection Method with Adaptive Temporal Distances for Offline Reinforcement Learning—0
Data-Driven Merton's Strategies via Policy Randomization—0
BadRL: Sparse Targeted Backdoor Attack Against Reinforcement LearningCode0
Active search and coverage using point-cloud reinforcement learning—0
Solving the swing-up and balance task for the Acrobot and Pendubot with SAC—0
Safeguarded Progress in Reinforcement Learning: Safe Bayesian Exploration for Control Policy Synthesis—0
Online Restless Multi-Armed Bandits with Long-Term Fairness Constraints—0
Improving Environment Robustness of Deep Reinforcement Learning Approaches for Autonomous Racing Using Bayesian Optimization-based Curriculum LearningCode0
Advancing RAN Slicing with Offline Reinforcement Learning—0
Fractional Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing—0
Imitate the Good and Avoid the Bad: An Incremental Approach to Safe Reinforcement LearningCode0
Assume-Guarantee Reinforcement Learning—0
Toward Computationally Efficient Inverse Reinforcement Learning via Reward Shaping—0
ReCoRe: Regularized Contrastive Representation Learning of World Model—0
Towards Automatic Data Augmentation for Disordered Speech Recognition—0
iOn-Profiler: intelligent Online multi-objective VNF Profiling with Reinforcement Learning—0
An Invitation to Deep Reinforcement Learning—0
Safe Exploration in Reinforcement Learning: Training Backup Control Barrier Functions with Zero Training Time Safety Violations—0
Toward a Reinforcement-Learning-Based System for Adjusting Medication to Minimize Speech Disfluency—0
Noise Distribution Decomposition based Multi-Agent Distributional Reinforcement Learning—0
Beyond Expected Return: Accounting for Policy Reproducibility when Evaluating Reinforcement Learning Algorithms—0
A dynamical clipping approach with task feedback for Proximal Policy OptimizationCode0
Building Open-Ended Embodied Agent via Language-Policy Bidirectional Adaptation—0
Learning Polynomial Representations of Physical Objects with Application to Certifying Correct Packing Configurations—0
KnowGPT: Knowledge Graph based Prompting for Large Language Models—0
Reward Certification for Policy Smoothed Reinforcement LearningCode0
Spreeze: High-Throughput Parallel Reinforcement Learning Framework—0
Partial End-to-end Reinforcement Learning for Robustness Against Modelling Error in Autonomous Racing—0
Modifying RL Policies with Imagined Actions: How Predictable Policies Can Enable Users to Perform Novel Tasks—0
Efficient Sparse-Reward Goal-Conditioned Reinforcement Learning with a High Replay Ratio and RegularizationCode0
PerfRL: A Small Language Model Framework for Efficient Code Optimization—0
On the calibration of compartmental epidemiological modelsCode0
Reinforcement Learning-Based Bionic Reflex Control for Anthropomorphic Robotic Grasping exploiting Domain Randomization—0
Modeling Risk in Reinforcement Learning: A Literature Mapping—0
Guaranteed Trust Region Optimization via Two-Phase KL Penalization—0
Exploring Parity Challenges in Reinforcement Learning through Curriculum Learning with Noisy LabelsCode0
CODEX: A Cluster-Based Method for Explainable Reinforcement LearningCode0
Is Feedback All You Need? Leveraging Natural Language Feedback in Goal-Conditioned Reinforcement LearningCode0
Learning to sample in Cartesian MRI—0
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Benchmark Results

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