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 4376–4400 of 15113 papers

TitleStatusHype
Adviser-Actor-Critic: Eliminating Steady-State Error in Reinforcement Learning Control—0
A dynamic game approach to training robust deep policies—0
A Dynamics Perspective of Pursuit-Evasion Games of Intelligent Agents with the Ability to Learn—0
AED: Automatic Discovery of Effective and Diverse Vulnerabilities for Autonomous Driving Policy with Large Language Models—0
Aerial Base Station Positioning and Power Control for Securing Communications: A Deep Q-Network Approach—0
Aesthetic Photo Collage with Deep Reinforcement Learning—0
A Fair Federated Learning Framework With Reinforcement Learning—0
A Family of Cognitively Realistic Parsing Environments for Deep Reinforcement Learning—0
A Family of Robust Stochastic Operators for Reinforcement Learning—0
A Federated Reinforcement Learning Framework for Link Activation in Multi-link Wi-Fi Networks—0
A Federated Reinforcement Learning Method with Quantization for Cooperative Edge Caching in Fog Radio Access Networks—0
A Few Expert Queries Suffices for Sample-Efficient RL with Resets and Linear Value Approximation—0
Affordance as general value function: A computational model—0
Affordance-based Reinforcement Learning for Urban Driving—0
Affordance-Guided Reinforcement Learning via Visual Prompting—0
A Finite-Sample Analysis of Distributionally Robust Average-Reward Reinforcement Learning—0
A finite time analysis of distributed Q-learning—0
A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation—0
A First-Occupancy Representation for Reinforcement Learning—0
A Flexible Measurement of Diversity in Datasets with Random Network Distillation—0
A Framework and Method for Online Inverse Reinforcement Learning—0
A Framework for Constrained and Adaptive Behavior-Based Agents—0
Scaling data-driven robotics with reward sketching and batch reinforcement learning—0
A Framework for dynamically meeting performance objectives on a service mesh—0
Learning Visual Robotic Control Efficiently with Contrastive Pre-training and Data Augmentation—0
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Benchmark Results

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