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 4426–4450 of 15113 papers

TitleStatusHype
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