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 6201–6250 of 15113 papers

TitleStatusHype
Learn Quasi-stationary Distributions of Finite State Markov Chain—0
Learn to Earn: Enabling Coordination within a Ride Hailing Fleet—0
Learn to Exceed: Stereo Inverse Reinforcement Learning with Concurrent Policy Optimization—0
Learn To Manage Portfolio With Reinforcement Learning—0
Learn to Match with No Regret: Reinforcement Learning in Markov Matching Markets—0
Learn to Play Tetris with Deep Reinforcement Learning—0
Learn to Teach: Sample-Efficient Privileged Learning for Humanoid Locomotion over Diverse Terrains—0
Learn to Tour: Operator Design For Solution Feasibility Mapping in Pickup-and-delivery Traveling Salesman Problem—0
Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning—0
Learn Zero-Constraint-Violation Policy in Model-Free Constrained Reinforcement Learning—0
Least-Squares Temporal Difference Learning for the Linear Quadratic Regulator—0
On the Model-Misspecification in Reinforcement Learning—0
Lecture Notes on Partially Known MDPs—0
LeDeepChef: Deep Reinforcement Learning Agent for Families of Text-Based Games—0
Score vs. Winrate in Score-Based Games: which Reward for Reinforcement Learning?—0
Lessons from reinforcement learning for biological representations of space—0
Lessons Learned from Data-Driven Building Control Experiments: Contrasting Gaussian Process-based MPC, Bilevel DeePC, and Deep Reinforcement Learning—0
Less Suboptimal Learning and Control in Variational POMDPs—0
Let's Reason Formally: Natural-Formal Hybrid Reasoning Enhances LLM's Math Capability—0
Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Tracking—0
Cognitive Level-k Meta-Learning for Safe and Pedestrian-Aware Autonomous Driving—0
Leverage the Average: an Analysis of KL Regularization in RL—0
Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning—0
Leveraging Domain-Unlabeled Data in Offline Reinforcement Learning across Two Domains—0
Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis—0
Leveraging human Domain Knowledge to model an empirical Reward function for a Reinforcement Learning problem—0
Leveraging human knowledge in tabular reinforcement learning: A study of human subjects—0
Leveraging Jumpy Models for Planning and Fast Learning in Robotic Domains—0
Leveraging Modality-specific Representations for Audio-visual Speech Recognition via Reinforcement Learning—0
Leveraging Offline Data in Online Reinforcement Learning—0
Leveraging Optimal Transport for Enhanced Offline Reinforcement Learning in Surgical Robotic Environments—0
Leveraging Partial SMILES Validation Scheme for Enhanced Drug Design in Reinforcement Learning Frameworks—0
PerfRL: A Small Language Model Framework for Efficient Code Optimization—0
Leveraging Reinforcement Learning for evaluating Robustness of KNN Search Algorithms—0
Leveraging Reinforcement Learning in Red Teaming for Advanced Ransomware Attack Simulations—0
Leveraging Reinforcement Learning Techniques for Effective Policy Adoption and Validation—0
Leveraging Reward Gradients For Reinforcement Learning in Differentiable Physics Simulations—0
Leveraging Sub-Optimal Data for Human-in-the-Loop Reinforcement Learning—0
Leveraging the Variance of Return Sequences for Exploration Policy—0
Leveraging Topological Maps in Deep Reinforcement Learning for Multi-Object Navigation—0
LgTS: Dynamic Task Sampling using LLM-generated sub-goals for Reinforcement Learning Agents—0
Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems—0
Lifelong Learning for Fog Load Balancing: A Transfer Learning Approach—0
Lifelong Reinforcement Learning with Temporal Logic Formulas and Reward Machines—0
Lifelong Robotic Reinforcement Learning by Retaining Experiences—0
Lifted Model Checking for Relational MDPs—0
Lifting the veil on hyper-parameters for value-based deep reinforcement learning—0
Policy Gradient Methods for Distortion Risk Measures—0
lilGym: Natural Language Visual Reasoning with Reinforcement Learning—0
LiMIIRL: Lightweight Multiple-Intent Inverse Reinforcement Learning—0
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Benchmark Results

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