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 3101–3125 of 15113 papers

TitleStatusHype
Overcoming Catastrophic Interference in Online Reinforcement Learning with Dynamic Self-Organizing Maps—0
Adversarial Deep Reinforcement Learning based Adaptive Moving Target Defense—0
Deep Reinforcement Learning-Based Adaptive IRS Control with Limited Feedback Codebooks—0
A Survey of Knowledge-based Sequential Decision Making under Uncertainty—0
A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress—0
AdaPool: A Diurnal-Adaptive Fleet Management Framework using Model-Free Deep Reinforcement Learning and Change Point Detection—0
A Survey of In-Context Reinforcement Learning—0
A Survey of Imitation Learning: Algorithms, Recent Developments, and Challenges—0
Cross Learning in Deep Q-Networks—0
Crossing the Human-Robot Embodiment Gap with Sim-to-Real RL using One Human Demonstration—0
Deep Reinforcement Learning Based Optimization for IRS Based UAV-NOMA Downlink Networks—0
Cross-Embodiment Dexterous Grasping with Reinforcement Learning—0
A Survey of Zero-shot Generalisation in Deep Reinforcement Learning—0
Cross-Domain Transfer via Semantic Skill Imitation—0
Cross-Domain Transfer in Reinforcement Learning using Target Apprentice—0
A Survey of Forex and Stock Price Prediction Using Deep Learning—0
A Survey of Exploration Methods in Reinforcement Learning—0
Acceleration of Actor-Critic Deep Reinforcement Learning for Visual Grasping in Clutter by State Representation Learning Based on Disentanglement of a Raw Input Image—0
CrossNorm: On Normalization for Off-Policy Reinforcement Learning—0
Cross-State Self-Constraint for Feature Generalization in Deep Reinforcement Learning—0
A survey of Monte Carlo methods for noisy and costly densities with application to reinforcement learning and ABC—0
Agent with Tangent-based Formulation and Anatomical Perception for Standard Plane Localization in 3D Ultrasound—0
Cross-Domain Perceptual Reward Functions—0
Crowd-PrefRL: Preference-Based Reward Learning from Crowds—0
A Survey of Explainable Reinforcement Learning—0
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Benchmark Results

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