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 5426–5450 of 15113 papers

TitleStatusHype
Instance-based Generalization in Reinforcement Learning—0
Instance-Dependent Complexity of Contextual Bandits and Reinforcement Learning: A Disagreement-Based Perspective—0
Instance-Dependent Confidence and Early Stopping for Reinforcement Learning—0
Instance-Dependent Near-Optimal Policy Identification in Linear MDPs via Online Experiment Design—0
Instigating Cooperation among LLM Agents Using Adaptive Information Modulation—0
Instructed Diffuser with Temporal Condition Guidance for Offline Reinforcement Learning—0
Instrumental Variable Value Iteration for Causal Offline Reinforcement Learning—0
Insulin Regimen ML-based control for T2DM patients—0
In Support of Over-Parametrization in Deep Reinforcement Learning: an Empirical Study—0
Insurance pricing on price comparison websites via reinforcement learning—0
Integral Equations and Machine Learning—0
Integrated and Adaptive Guidance and Control for Endoatmospheric Missiles via Reinforcement Learning—0
Integrated Decision and Control at Multi-Lane Intersections with Mixed Traffic Flow—0
Integrated Decision and Control for High-Level Automated Vehicles by Mixed Policy Gradient and Its Experiment Verification—0
Integrated Drill Boom Hole-Seeking Control via Reinforcement Learning—0
Integrated Learning of Dialog Strategies and Semantic Parsing—0
Integrated Longitudinal Speed Decision-Making and Energy Efficiency Control for Connected Electrified Vehicles—0
Integrating Behavior Cloning and Reinforcement Learning for Improved Performance in Dense and Sparse Reward Environments—0
Integrating Domain Knowledge for handling Limited Data in Offline RL—0
Integrating Episodic Memory into a Reinforcement Learning Agent using Reservoir Sampling—0
Integrating Human Knowledge Through Action Masking in Reinforcement Learning for Operations Research—0
Integrating independent and centralized multi-agent reinforcement learning for traffic signal network optimization—0
Integrating Large Language Models and Reinforcement Learning for Non-Linear Reasoning—0
Integrating Learning-Based Manipulation and Physics-Based Locomotion for Whole-Body Badminton Robot Control—0
Integrating LEO Satellite and UAV Relaying via Reinforcement Learning for Non-Terrestrial Networks—0
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Benchmark Results

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