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 11501–11550 of 15113 papers

TitleStatusHype
Adaptive Modulation and Coding based on Reinforcement Learning for 5G Networks—0
Biologically inspired architectures for sample-efficient deep reinforcement learning—0
Deep Reinforcement Learning for Multi-Driver Vehicle Dispatching and Repositioning Problem—0
Theory-based Causal Transfer: Integrating Instance-level Induction and Abstract-level Structure Learning—0
Mitigate Bias in Face Recognition using Skewness-Aware Reinforcement Learning—0
End-to-End Model-Free Reinforcement Learning for Urban Driving using Implicit AffordancesCode0
A Deep Reinforcement Learning Architecture for Multi-stage Optimal Control—0
Learning to Optimize Variational Quantum Circuits to Solve Combinatorial ProblemsCode0
Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms—0
Which Channel to Ask My Question? Personalized Customer Service RequestStream Routing using DeepReinforcement Learning—0
Scaling active inference—0
ORL: Reinforcement Learning Benchmarks for Online Stochastic Optimization ProblemsCode1
Dynamic Control of a Fiber Manufacturing Process using Deep Reinforcement LearningCode0
Corpus-Level End-to-End Exploration for Interactive SystemsCode0
From Persistent Homology to Reinforcement Learning with Applications for Retail Banking—0
Iteratively-Refined Interactive 3D Medical Image Segmentation with Multi-Agent Reinforcement Learning—0
Deep Reinforcement Learning for Trading—0
Graph Pruning for Model Compression—0
Analysis of Evolutionary Behavior in Self-Learning Media Search Engines—0
DeepSynth: Automata Synthesis for Automatic Task Segmentation in Deep Reinforcement LearningCode0
Fleet Control using Coregionalized Gaussian Process Policy IterationCode0
Information-Theoretic Confidence Bounds for Reinforcement Learning—0
Accelerating Reinforcement Learning with Suboptimal Guidance—0
Efficient Drone Mobility Support Using Reinforcement Learning—0
State Alignment-based Imitation Learning—0
Memory-Efficient Episodic Control Reinforcement Learning with Dynamic Online k-meansCode0
Sample-Efficient Reinforcement Learning with Maximum Entropy Mellowmax Episodic ControlCode0
Agent Probing Interaction Policies—0
Deep Reinforcement Learning in Cryptocurrency Market Making—0
A Tale of Two-Timescale Reinforcement Learning with the Tightest Finite-Time Bound—0
Safe Policies for Reinforcement Learning via Primal-Dual Methods—0
Solving Online Threat Screening Games using Constrained Action Space Reinforcement Learning—0
On Policy Learning Robust to Irreversible Events: An Application to Robotic In-Hand Manipulation—0
Bayesian Curiosity for Efficient Exploration in Reinforcement LearningCode0
Corruption-robust exploration in episodic reinforcement learning—0
Avoiding Jammers: A Reinforcement Learning Approach—0
Hierarchical Average Reward Policy Gradient Algorithms—0
Generalizable Resource Allocation in Stream Processing via Deep Reinforcement LearningCode0
Efficient decorrelation of features using Gramian in Reinforcement Learning—0
Attention-Privileged Reinforcement Learning—0
Decision Making for Autonomous Driving via Augmented Adversarial Inverse Reinforcement Learning—0
MANGA: Method Agnostic Neural-policy Generalization and Adaptation—0
Variance Reduced Advantage Estimation with δ Hindsight Credit Assignment—0
Planning with Goal-Conditioned PoliciesCode0
Placement Optimization of Aerial Base Stations with Deep Reinforcement Learning—0
Efficient Exploration through Intrinsic Motivation Learning for Unsupervised Subgoal Discovery in Model-Free Hierarchical Reinforcement Learning—0
Comments on the Du-Kakade-Wang-Yang Lower Bounds—0
Influence-aware Memory Architectures for Deep Reinforcement LearningCode0
Inducing Cooperation via Team Regret Minimization based Multi-Agent Deep Reinforcement Learning—0
Unsupervised Reinforcement Learning of Transferable Meta-Skills for Embodied Navigation—0
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Benchmark Results

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