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 1926–1950 of 15113 papers

TitleStatusHype
Enhanced Gene Selection in Single-Cell Genomics: Pre-Filtering Synergy and Reinforced Optimization—0
Learning Reward and Policy Jointly from Demonstration and Preference Improves Alignment—0
Integrating Domain Knowledge for handling Limited Data in Offline RL—0
Sample Complexity Reduction via Policy Difference Estimation in Tabular Reinforcement Learning—0
CHARME: A chain-based reinforcement learning approach for the minor embedding problem—0
Decoupling regularization from the action spaceCode0
Investigating Pre-Training Objectives for Generalization in Vision-Based Reinforcement LearningCode1
Is Value Functions Estimation with Classification Plug-and-play for Offline Reinforcement Learning?Code0
Deep Multi-Objective Reinforcement Learning for Utility-Based Infrastructural Maintenance OptimizationCode0
Discovering Multiple Solutions from a Single Task in Offline Reinforcement Learning—0
EXPIL: Explanatory Predicate Invention for Learning in GamesCode0
STARLING: Self-supervised Training of Text-based Reinforcement Learning Agent with Large Language ModelsCode0
ICU-Sepsis: A Benchmark MDP Built from Real Medical DataCode1
Enhanced Flight Envelope Protection: A Novel Reinforcement Learning Approach—0
Decision Mamba: A Multi-Grained State Space Model with Self-Evolution Regularization for Offline RLCode0
Diffusion-based Reinforcement Learning for Dynamic UAV-assisted Vehicle Twins Migration in Vehicular Metaverses—0
Optimizing Automatic Differentiation with Deep Reinforcement Learning—0
Sim-to-Real Transfer of Deep Reinforcement Learning Agents for Online Coverage Path Planning—0
Primitive Agentic First-Order Optimization—0
Proofread: Fixes All Errors with One Tap—0
Strategically Conservative Q-LearningCode1
Excluding the Irrelevant: Focusing Reinforcement Learning through Continuous Action Masking—0
Breeding Programs Optimization with Reinforcement Learning—0
Optimizing Autonomous Driving for Safety: A Human-Centric Approach with LLM-Enhanced RLHF—0
Deterministic Uncertainty Propagation for Improved Model-Based Offline Reinforcement LearningCode0
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Benchmark Results

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