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 4751–4775 of 15113 papers

TitleStatusHype
ANT: Learning Accurate Network Throughput for Better Adaptive Video Streaming—0
An Understanding of Learning from Demonstrations for Neural Text Generation—0
An Unsupervised Anomaly Detection in Electricity Consumption Using Reinforcement Learning and Time Series Forest Based Framework—0
AnyMorph: Learning Transferable Polices By Inferring Agent Morphology—0
Anytime PSRO for Two-Player Zero-Sum Games—0
AoA-Based Pilot Assignment in Massive MIMO Systems Using Deep Reinforcement Learning—0
AoI Minimization in Status Update Control with Energy Harvesting Sensors—0
A PAC RL Algorithm for Episodic POMDPs—0
A pedestrian path-planning model in accordance with obstacle's danger with reinforcement learning—0
APF+: Boosting adaptive-potential function reinforcement learning methods with a W-shaped network for high-dimensional games—0
Breaking the Curse of Dimensionality in Multiagent State Space: A Unified Agent Permutation Framework—0
A Policy Efficient Reduction Approach to Convex Constrained Deep Reinforcement Learning—0
A Policy Search Method For Temporal Logic Specified Reinforcement Learning Tasks—0
ApolloRL: a Reinforcement Learning Platform for Autonomous Driving—0
GEC: A Unified Framework for Interactive Decision Making in MDP, POMDP, and Beyond—0
AppBuddy: Learning to Accomplish Tasks in Mobile Apps via Reinforcement Learning—0
Applicability and Challenges of Deep Reinforcement Learning for Satellite Frequency Plan Design—0
Application of deep reinforcement learning for Indian stock trading automation—0
Application of Deep Reinforcement Learning to Payment Fraud—0
Application of Multi-Agent Reinforcement Learning for Battery Management in Renewable Mini-Grids—0
Application of Reinforcement Learning for 5G Scheduling Parameter Optimization—0
Twin actor twin delayed deep deterministic policy gradient (TATD3) learning for batch process control—0
Applications of Deep Learning and Reinforcement Learning to Biological Data—0
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey—0
Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey—0
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Benchmark Results

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