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 3976–4000 of 15113 papers

TitleStatusHype
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