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 2801–2850 of 15113 papers

TitleStatusHype
Offline RL with Observation Histories: Analyzing and Improving Sample Complexity—0
Sample-Efficient and Safe Deep Reinforcement Learning via Reset Deep Ensemble Agents—0
Closed Drafting as a Case Study for First-Principle Interpretability, Memory, and Generalizability in Deep Reinforcement Learning—0
Unleashing the Power of Pre-trained Language Models for Offline Reinforcement LearningCode1
Diversify & Conquer: Outcome-directed Curriculum RL via Out-of-Distribution Disagreement—0
Variational Curriculum Reinforcement Learning for Unsupervised Discovery of SkillsCode1
Posterior Sampling for Competitive RL: Function Approximation and Partial Observation—0
On the Theory of Risk-Aware Agents: Bridging Actor-Critic and Economics—0
DrM: Mastering Visual Reinforcement Learning through Dormant Ratio MinimizationCode1
Posterior Sampling with Delayed Feedback for Reinforcement Learning with Linear Function Approximation—0
Behavior Alignment via Reward Function Optimization—0
Automaton Distillation: Neuro-Symbolic Transfer Learning for Deep Reinforcement Learning—0
Language Agents with Reinforcement Learning for Strategic Play in the Werewolf Game—0
MAG-GNN: Reinforcement Learning Boosted Graph Neural Network—0
Spacecraft Autonomous Decision-Planning for Collision Avoidance: a Reinforcement Learning Approach—0
Real-World Implementation of Reinforcement Learning Based Energy Coordination for a Cluster of Households—0
Unsupervised Behavior Extraction via Random Intent Priors—0
Robust Offline Reinforcement learning with Heavy-Tailed RewardsCode0
Benchmark Generation Framework with Customizable Distortions for Image Classifier RobustnessCode0
Train Once, Get a Family: State-Adaptive Balances for Offline-to-Online Reinforcement LearningCode1
Deep Reinforcement Learning for Weapons to Targets Assignment in a Hypersonic strike—0
Bridging Distributionally Robust Learning and Offline RL: An Approach to Mitigate Distribution Shift and Partial Data CoverageCode0
Guided Data Augmentation for Offline Reinforcement Learning and Imitation Learning—0
Counterfactual-Augmented Importance Sampling for Semi-Offline Policy EvaluationCode0
Understanding when Dynamics-Invariant Data Augmentations Benefit Model-Free Reinforcement Learning UpdatesCode0
Demonstration-Regularized RL—0
Grow Your Limits: Continuous Improvement with Real-World RL for Robotic Locomotion—0
CROP: Conservative Reward for Model-based Offline Policy OptimizationCode1
CQM: Curriculum Reinforcement Learning with a Quantized World Model—0
Transfer of Reinforcement Learning-Based Controllers from Model- to Hardware-in-the-Loop—0
Controlled Decoding from Language Models—0
Privately Aligning Language Models with Reinforcement Learning—0
Model-enhanced Contrastive Reinforcement Learning for Sequential Recommendation—0
MultiPrompter: Cooperative Prompt Optimization with Multi-Agent Reinforcement Learning—0
TD-MPC2: Scalable, Robust World Models for Continuous ControlCode2
Hyperparameter Optimization for Multi-Objective Reinforcement Learning—0
A Contextualized Real-Time Multimodal Emotion Recognition for Conversational Agents using Graph Convolutional Networks in Reinforcement Learning—0
Fractal Landscapes in Policy Optimization—0
Finetuning Offline World Models in the Real World—0
WebWISE: Web Interface Control and Sequential Exploration with Large Language Models—0
Corruption-Robust Offline Reinforcement Learning with General Function ApproximationCode0
Enhancing Robotic Manipulation: Harnessing the Power of Multi-Task Reinforcement Learning and Single Life Reinforcement Learning in Meta-World—0
Safe Navigation: Training Autonomous Vehicles using Deep Reinforcement Learning in CARLACode1
A Review of Reinforcement Learning for Natural Language Processing, and Applications in Healthcare—0
Diversify Question Generation with Retrieval-Augmented Style TransferCode1
Reinforcement learning in large, structured action spaces: A simulation study of decision support for spinal cord injury rehabilitation—0
Diverse Priors for Deep Reinforcement Learning—0
Iteratively Learn Diverse Strategies with State Distance Information—0
Provable Benefits of Multi-task RL under Non-Markovian Decision Making Processes—0
Automatic Unit Test Data Generation and Actor-Critic Reinforcement Learning for Code SynthesisCode1
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Benchmark Results

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