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 7151–7175 of 15113 papers

TitleStatusHype
Improving Generalization of Deep Reinforcement Learning-based TSP Solvers—0
Scalable Multi-Agent Reinforcement Learning for Residential Load Scheduling under Data Governance—0
Hierarchical Potential-based Reward Shaping from Task SpecificationsCode0
Adaptive control of a mechatronic system using constrained residual reinforcement learning—0
Deep Reinforcement Learning for Solving the Heterogeneous Capacitated Vehicle Routing ProblemCode1
Heterogeneous Attentions for Solving Pickup and Delivery Problem via Deep Reinforcement Learning—0
Decentralized Cooperative Lane Changing at Freeway Weaving Areas Using Multi-Agent Deep Reinforcement Learning—0
Deep reinforcement learning for guidewire navigation in coronary artery phantom—0
CARL: A Benchmark for Contextual and Adaptive Reinforcement LearningCode1
DeepEdge: A Deep Reinforcement Learning based Task Orchestrator for Edge Computing—0
A Deep Reinforcement Learning Framework for Contention-Based Spectrum Sharing—0
A study of first-passage time minimization via Q-learning in heated gridworlds—0
Dropout Q-Functions for Doubly Efficient Reinforcement LearningCode1
OTTR: Off-Road Trajectory Tracking using Reinforcement Learning—0
NaRLE: Natural Language Models using Reinforcement Learning with Emotion Feedback—0
Mining for Potent Inhibitors through Artificial Intelligence and Physics: A Unified Methodology for Ligand Based and Structure Based Drug Design—0
You Only Evaluate Once: a Simple Baseline Algorithm for Offline RL—0
Multi-Agent Path Planning Using Deep Reinforcement Learning—0
Uncertainty-Based Offline Reinforcement Learning with Diversified Q-EnsembleCode1
Reinforcement Learning for Admission Control in Wireless Virtual Network Embedding—0
Large Batch Experience ReplayCode1
Behaviour-conditioned policies for cooperative reinforcement learning tasks—0
A Modified Q-Learning Algorithm for Rate-Profiling of Polarization Adjusted Convolutional (PAC) Codes—0
Learning to Assist Agents by Observing Them—0
Hit and Lead Discovery with Explorative RL and Fragment-based Molecule Generation—0
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Benchmark Results

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