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 8851–8900 of 15113 papers

TitleStatusHype
Experience-Based Heuristic Search: Robust Motion Planning with Deep Q-Learning—0
Deceptive Reinforcement Learning for Privacy-Preserving Planning—0
Finite Sample Analysis of Minimax Offline Reinforcement Learning: Completeness, Fast Rates and First-Order Efficiency—0
Deep reinforcement learning for smart calibration of radio telescopesCode0
Revisiting Prioritized Experience Replay: A Value PerspectiveCode0
Provably Efficient Algorithms for Multi-Objective Competitive RL—0
Persistent Rule-based Interactive Reinforcement Learning—0
A review of motion planning algorithms for intelligent robotics—0
Deep reinforcement learning-based image classification achieves perfect testing set accuracy for MRI brain tumors with a training set of only 30 images—0
Hybrid Adversarial Imitation Learning—0
Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agentsCode1
How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learned—0
A deep learning model for gas storage optimization—0
The Pitfall of More Powerful Autoencoders in Lidar-Based Navigation—0
Neural Recursive Belief States in Multi-Agent Reinforcement Learning—0
Multi-UAV Mobile Edge Computing and Path Planning Platform based on Reinforcement Learning—0
Near-Optimal Offline Reinforcement Learning via Double Variance Reduction—0
A step toward a reinforcement learning de novo genome assembler—0
Improving Reinforcement Learning with Human Assistance: An Argument for Human Subject Studies with HIPPO Gym—0
A Lyapunov Theory for Finite-Sample Guarantees of Asynchronous Q-Learning and TD-Learning Variants—0
An Abstraction-based Method to Check Multi-Agent Deep Reinforcement-Learning Behaviors—0
Approximately Solving Mean Field Games via Entropy-Regularized Deep Reinforcement Learning—0
Metrics and continuity in reinforcement learningCode0
Towards Multi-agent Reinforcement Learning for Wireless Network Protocol Synthesis—0
Reinforcement Learning with Probabilistic Boolean Network Models of Smart Grid Devices—0
Throughput Optimization for Grant-Free Multiple Access With Multiagent Deep Reinforcement Learning—0
Variation-resistant Q-learning: Controlling and Utilizing Estimation Bias in Reinforcement Learning for Better PerformanceCode0
NeoRL: A Near Real-World Benchmark for Offline Reinforcement LearningCode1
Risk Aware and Multi-Objective Decision Making with Distributional Monte Carlo Tree Search—0
Multi-Agent Reinforcement Learning with Temporal Logic SpecificationsCode1
Interpretable Reinforcement Learning Inspired by Piaget's Theory of Cognitive Development—0
Hybrid Beamforming for mmWave MU-MISO Systems Exploiting Multi-agent Deep Reinforcement Learning—0
Hybrid Information-driven Multi-agent Reinforcement Learning—0
Bellman Eluder Dimension: New Rich Classes of RL Problems, and Sample-Efficient Algorithms—0
A Secure Learning Control Strategy via Dynamic Camouflaging for Unknown Dynamical Systems under Attacks—0
Improving Human Decision-Making by Discovering Efficient Strategies for Hierarchical Planning—0
Fast Rates for the Regret of Offline Reinforcement Learning—0
Contextualized Rewriting for Text SummarizationCode1
Deep Reinforcement Learning Aided Monte Carlo Tree Search for MIMO Detection—0
Deep Reinforcement Learning-Based Product Recommender for Online Advertising—0
On the Stability of Random Matrix Product with Markovian Noise: Application to Linear Stochastic Approximation and TD Learning—0
Learning Skills to Navigate without a Master: A Sequential Multi-Policy Reinforcement Learning Algorithm—0
Policy Mirror Descent for Reinforcement Learning: Linear Convergence, New Sampling Complexity, and Generalized Problem Classes—0
Meta-Reinforcement Learning for Reliable Communication in THz/VLC Wireless VR NetworksCode0
Thermal Control of Laser Powder Bed Fusion Using Deep Reinforcement Learning—0
Scalable Voltage Control using Structure-Driven Hierarchical Deep Reinforcement Learning—0
Reinforcement Learning for Freight Booking Control Problems—0
Challenges for Using Impact Regularizers to Avoid Negative Side Effects—0
Counterfactual State Explanations for Reinforcement Learning Agents via Generative Deep LearningCode0
Learning-based vs Model-free Adaptive Control of a MAV under Wind Gust—0
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Benchmark Results

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