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 2101–2125 of 15113 papers

TitleStatusHype
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
Hype or Heuristic? Quantum Reinforcement Learning for Join Order OptimisationCode0
Neural Network Compression for Reinforcement Learning Tasks—0
Reducing Risk for Assistive Reinforcement Learning Policies with Diffusion Models—0
Near-Optimal Regret in Linear MDPs with Aggregate Bandit Feedback—0
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
Improving Targeted Molecule Generation through Language Model Fine-Tuning Via Reinforcement Learning—0
Dominion: A New Frontier for AI Research—0
Value Augmented Sampling for Language Model Alignment and PersonalizationCode1
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
Roadside Units Assisted Localized Automated Vehicle Maneuvering: An Offline Reinforcement Learning Approach—0
DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language ModelCode9
Human-centric Reward Optimization for Reinforcement Learning-based Automated Driving using Large Language ModelsCode1
Genetic Drift Regularization: on preventing Actor Injection from breaking Evolution Strategies—0
ACEGEN: Reinforcement learning of generative chemical agents for drug discoveryCode3
SwiftRL: Towards Efficient Reinforcement Learning on Real Processing-In-Memory SystemsCode0
Improving Offline Reinforcement Learning with Inaccurate Simulators—0
Out-of-Distribution Adaptation in Offline RL: Counterfactual Reasoning via Causal Normalizing Flows—0
Reverse Forward Curriculum Learning for Extreme Sample and Demonstration Efficiency in Reinforcement LearningCode2
Safe Reinforcement Learning with Learned Non-Markovian Safety Constraints—0
UDUC: An Uncertainty-driven Approach for Learning-based Robust Control—0
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Benchmark Results

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