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 2601–2625 of 15113 papers

TitleStatusHype
A Collaborative Multi-agent Reinforcement Learning Framework for Dialog Action Decomposition—0
A Machine Learning Approach to Routing—0
Reinforcement Learning with Non-Markovian Rewards—0
Coordinated Multi-Agent Exploration Using Shared Goals—0
Coordinated Random Access for Industrial IoT With Correlated Traffic By Reinforcement-Learning—0
Coordinating Disaster Emergency Response with Heuristic Reinforcement Learning—0
Autonomous navigation of catheters and guidewires in mechanical thrombectomy using inverse reinforcement learning—0
Autonomous Navigation of an Ultrasound Probe Towards Standard Scan Planes with Deep Reinforcement Learning—0
A Machine Learning Approach for Task and Resource Allocation in Mobile Edge Computing Based Networks—0
A Machine Learning Approach for Prosumer Management in Intraday Electricity Markets—0
Autonomous Maintenance in IoT Networks via AoI-driven Deep Reinforcement Learning—0
Adaptive Multi-pass Decoder for Neural Machine Translation—0
A Cognitive Architecture Based on a Learning Classifier System with Spiking Classifiers—0
Autonomous Learning of Features for Control: Experiments with Embodied and Situated Agents—0
Autonomous Industrial Management via Reinforcement Learning: Self-Learning Agents for Decision-Making -- A Review—0
A Lyapunov Theory for Finite-Sample Guarantees of Asynchronous Q-Learning and TD-Learning Variants—0
Autonomous Highway Driving using Deep Reinforcement Learning—0
Autonomous Extraction of a Hierarchical Structure of Tasks in Reinforcement Learning, A Sequential Associate Rule Mining Approach—0
A Lyapunov Drift-Plus-Penalty Method Tailored for Reinforcement Learning with Queue Stability—0
Adaptive Multi-model Fusion Learning for Sparse-Reward Reinforcement Learning—0
Autonomous Extracting a Hierarchical Structure of Tasks in Reinforcement Learning and Multi-task Reinforcement Learning—0
Adaptive Multi-Fidelity Reinforcement Learning for Variance Reduction in Engineering Design Optimization—0
Autonomous Drone Swarm Navigation and Multi-target Tracking in 3D Environments with Dynamic Obstacles—0
Autonomous Drone Racing with Deep Reinforcement Learning—0
AltGraph: Redesigning Quantum Circuits Using Generative Graph Models for Efficient Optimization—0
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Benchmark Results

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