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

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Showing 3951–4000 of 15113 papers

TitleStatusHype
A Multimodal Learning-based Approach for Autonomous Landing of UAV—0
Rethinking Robustness Assessment: Adversarial Attacks on Learning-based Quadrupedal Locomotion Controllers—0
Practical and efficient quantum circuit synthesis and transpiling with Reinforcement Learning—0
Multi-Agent Reinforcement Learning with Hierarchical Coordination for Emergency Responder Stationing—0
Highway Graph to Accelerate Reinforcement LearningCode0
Learning Future Representation with Synthetic Observations for Sample-efficient Reinforcement Learning—0
Investigating the Impact of Choice on Deep Reinforcement Learning for Space Controls—0
Scrutinize What We Ignore: Reining In Task Representation Shift Of Context-Based Offline Meta Reinforcement LearningCode0
Large Language Models are Biased Reinforcement LearnersCode0
Do No Harm: A Counterfactual Approach to Safe Reinforcement Learning—0
Comparisons Are All You Need for Optimizing Smooth Functions—0
Towards Robust Policy: Enhancing Offline Reinforcement Learning with Adversarial Attacks and Defenses—0
Optimal control barrier functions for RL based safe powertrain control—0
Combined film and pulse heating of lithium ion batteries to improve performance in low ambient temperature—0
LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions—0
Stochastic Q-learning for Large Discrete Action Spaces—0
Simulation-Based Benchmarking of Reinforcement Learning Agents for Personalized Retail PromotionsCode0
Fine-Tuning Large Vision-Language Models as Decision-Making Agents via Reinforcement Learning—0
Deep Learning in Earthquake Engineering: A Comprehensive Review—0
Fast Two-Time-Scale Stochastic Gradient Method with Applications in Reinforcement Learning—0
IM-RAG: Multi-Round Retrieval-Augmented Generation Through Learning Inner Monologues—0
vMFER: Von Mises-Fisher Experience Resampling Based on Uncertainty of Gradient Directions for Policy Improvement—0
Deep Reinforcement Learning for Real-Time Ground Delay Program Revision and Corresponding Flight Delay Assignments—0
Neural Network Compression for Reinforcement Learning Tasks—0
Hype or Heuristic? Quantum Reinforcement Learning for Join Order OptimisationCode0
Near-Optimal Regret in Linear MDPs with Aggregate Bandit Feedback—0
Reducing Risk for Assistive Reinforcement Learning Policies with Diffusion Models—0
Intrinsic Rewards for Exploration without Harm from Observational Noise: A Simulation Study Based on the Free Energy Principle—0
CAGES: Cost-Aware Gradient Entropy Search for Efficient Local Multi-Fidelity Bayesian OptimizationCode0
Ensemble Successor Representations for Task Generalization in Offline-to-Online Reinforcement Learning—0
Fairness in Reinforcement Learning: A Survey—0
Space Processor Computation Time Analysis for Reinforcement Learning and Run Time Assurance Control Policies—0
Dominion: A New Frontier for AI Research—0
Improving Targeted Molecule Generation through Language Model Fine-Tuning Via Reinforcement Learning—0
An Overview of Machine Learning-Enabled Optimization for Reconfigurable Intelligent Surfaces-Aided 6G Networks: From Reinforcement Learning to Large Language Models—0
Fast Stochastic Policy Gradient: Negative Momentum for Reinforcement Learning—0
Genetic Drift Regularization: on preventing Actor Injection from breaking Evolution Strategies—0
SwiftRL: Towards Efficient Reinforcement Learning on Real Processing-In-Memory SystemsCode0
Roadside Units Assisted Localized Automated Vehicle Maneuvering: An Offline Reinforcement Learning Approach—0
Improving Offline Reinforcement Learning with Inaccurate Simulators—0
Out-of-Distribution Adaptation in Offline RL: Counterfactual Reasoning via Causal Normalizing Flows—0
Safe Reinforcement Learning with Learned Non-Markovian Safety Constraints—0
CTD4 -- A Deep Continuous Distributional Actor-Critic Agent with a Kalman Fusion of Multiple CriticsCode0
UDUC: An Uncertainty-driven Approach for Learning-based Robust Control—0
Learning Robot Soccer from Egocentric Vision with Deep Reinforcement Learning—0
Zero-Sum Positional Differential Games as a Framework for Robust Reinforcement Learning: Deep Q-Learning Approach—0
A Model-based Multi-Agent Personalized Short-Video Recommender System—0
Learning Robust Autonomous Navigation and Locomotion for Wheeled-Legged Robots—0
Model-based reinforcement learning for protein backbone design—0
Proximal Curriculum with Task Correlations for Deep Reinforcement LearningCode0
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Benchmark Results

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