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 7901–7950 of 15113 papers

TitleStatusHype
Branch Prediction as a Reinforcement Learning Problem: Why, How and Case Studies—0
Control of a Mixed Autonomy Signalised Urban Intersection: An Action-Delayed Reinforcement Learning Approach—0
Brax -- A Differentiable Physics Engine for Large Scale Rigid Body SimulationCode2
Density Constrained Reinforcement Learning—0
Model-Based Reinforcement Learning via Latent-Space CollocationCode1
Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy EvaluationCode1
Hierarchically Integrated Models: Learning to Navigate from Heterogeneous Robots—0
The Option Keyboard: Combining Skills in Reinforcement Learning—0
Reinforcement Learning-based Dialogue Guided Event Extraction to Exploit Argument RelationsCode1
Evolving Hierarchical Memory-Prediction Machines in Multi-Task Reinforcement Learning—0
Bregman Gradient Policy OptimizationCode0
Uncertainty-Aware Model-Based Reinforcement Learning with Application to Autonomous Driving—0
Provably Efficient Representation Selection in Low-rank Markov Decision Processes: From Online to Offline RL—0
Uniform-PAC Bounds for Reinforcement Learning with Linear Function Approximation—0
Local policy search with Bayesian optimizationCode1
Off-Policy Reinforcement Learning with Delayed Rewards—0
MMD-MIX: Value Function Factorisation with Maximum Mean Discrepancy for Cooperative Multi-Agent Reinforcement Learning—0
Variance-Aware Off-Policy Evaluation with Linear Function Approximation—0
Reinforcement Learning for Physical Layer CommunicationsCode0
Agnostic Reinforcement Learning with Low-Rank MDPs and Rich Observations—0
A Reduction-Based Framework for Conservative Bandits and Reinforcement Learning—0
Lifted Model Checking for Relational MDPs—0
Distributed Heuristic Multi-Agent Path Finding with CommunicationCode1
Cogment: Open Source Framework For Distributed Multi-actor Training, Deployment & Operations—0
Emphatic Algorithms for Deep Reinforcement Learning—0
Interpretable Model-based Hierarchical Reinforcement Learning using Inductive Logic Programming—0
Reinforcement Learning for Resource Allocation in Steerable Laser-based Optical Wireless Systems—0
Policy Smoothing for Provably Robust Reinforcement Learning—0
OptiDICE: Offline Policy Optimization via Stationary Distribution Correction EstimationCode1
Scientific multi-agent reinforcement learning for wall-models of turbulent flows—0
Analytically Tractable Bayesian Deep Q-Learning—0
Unsupervised Learning for Robust Fitting: A Reinforcement Learning Approach—0
Video Summarization through Reinforcement Learning with a 3D Spatio-Temporal U-Net—0
Boosting Offline Reinforcement Learning with Residual Generative Modeling—0
A Max-Min Entropy Framework for Reinforcement LearningCode1
Adversarially Trained Neural Policies in the Fourier Domain—0
Towards Safe Reinforcement Learning via Constraining Conditional Value at RiskCode1
Non-Robust Feature Mapping in Deep Reinforcement Learning—0
Strategically-timed State-Observation Attacks on Deep Reinforcement Learning Agents—0
Sample Efficient Social Navigation Using Inverse Reinforcement Learning—0
Scenic4RL: Programmatic Modeling and Generation of Reinforcement Learning Environments—0
Proper Value EquivalenceCode0
MADE: Exploration via Maximizing Deviation from Explored RegionsCode1
The Curse of Passive Data Collection in Batch Reinforcement Learning—0
Deep Reinforcement Learning Models Predict Visual Responses in the Brain: A Preliminary Result—0
Goal-Directed Planning by Reinforcement Learning and Active Inference—0
Deep reinforcement learning with automated label extraction from clinical reports accurately classifies 3D MRI brain volumes—0
A Reinforcement Learning Approach for an IRS-assisted NOMA Network—0
Adapting the Function Approximation Architecture in Online Reinforcement Learning—0
Deep Reinforcement Learning Based Optimization for IRS Based UAV-NOMA Downlink Networks—0
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Benchmark Results

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