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 11251–11300 of 15113 papers

TitleStatusHype
A storage expansion planning framework using reinforcement learning and simulation-based optimization—0
Deep Interactive Reinforcement Learning for Path Following of Autonomous Underwater Vehicle—0
Reinforcement Learning Tracking Control for Robotic Manipulator With Kernel-Based Dynamic Model—0
On Computation and Generalization of Generative Adversarial Imitation Learning—0
Population-Guided Parallel Policy Search for Reinforcement LearningCode1
Distributional Soft Actor-Critic: Off-Policy Reinforcement Learning for Addressing Value Estimation ErrorsCode1
EEG-based Drowsiness Estimation for Driving Safety using Deep Q-Learning—0
A Nonparametric Off-Policy Policy GradientCode0
On Thompson Sampling for Smoother-than-Lipschitz Bandits—0
Perception and Navigation in Autonomous Systems in the Era of Learning: A Survey—0
Sample-based Distributional Policy Gradient—0
Multi-Agent Deep Reinforcement Learning for Cooperative Connected Vehicles—0
Decentralized Automotive Radar Spectrum Allocation to Avoid Mutual Interference Using Reinforcement Learning—0
Deep Reinforcement Learning for Active Human Pose EstimationCode1
Blue River Controls: A toolkit for Reinforcement Learning Control Systems on HardwareCode1
Reinforcement Learning via Fenchel-Rockafellar DualityCode1
Optimal Options for Multi-Task Reinforcement Learning Under Time Constraints—0
Experimental Analysis of Reinforcement Learning Techniques for Spectrum Sharing Radar—0
Learning Reusable Options for Multi-Task Reinforcement Learning—0
High-speed Autonomous Drifting with Deep Reinforcement Learning—0
Generalizing Emergent Communication—0
A Boolean Task Algebra for Reinforcement LearningCode1
Universal Successor Features for Transfer Reinforcement Learning—0
MushroomRL: Simplifying Reinforcement Learning ResearchCode1
Represented Value Function Approach for Large Scale Multi Agent Reinforcement LearningCode1
Hierarchical Reinforcement Learning as a Model of Human Task Interleaving—0
Intelligent Roundabout Insertion using Deep Reinforcement Learning—0
Making Sense of Reinforcement Learning and Probabilistic Inference—0
Zero-Shot Reinforcement Learning with Deep Attention Convolutional Neural Networks—0
Continuous-Discrete Reinforcement Learning for Hybrid Control in Robotics—0
Joint Goal and Strategy Inference across Heterogeneous Demonstrators via Reward Network Distillation—0
An Optimistic Perspective on Offline Deep Reinforcement LearningCode1
Generative Adversarial Imitation Learning with Neural Network Parameterization: Global Optimality and Convergence Rate—0
Breaking the Curse of Many Agents: Provable Mean Embedding Q-Iteration for Mean-Field Reinforcement Learning—0
CoMic: Co-Training and Mimicry for Reusable Skills—0
Inductive Bias-driven Reinforcement Learning For Efficient Schedules in Heterogeneous Clusters—0
A Game Theoretic Perspective on Model-Based Reinforcement Learning—0
Adaptive Droplet Routing in Digital Microfluidic Biochips Using Deep Reinforcement Learning—0
Batch Reinforcement Learning with Hyperparameter Gradients—0
Designing Optimal Dynamic Treatment Regimes: A Causal Reinforcement Learning Approach—0
Learning General-Purpose Controllers via Locally Communicating Sensorimotor Modules—0
Learning Fair Policies in Multi-Objective (Deep) Reinforcement Learning with Average and Discounted Rewards—0
Deep Reinforcement Learning with Smooth Policy—0
Bridging the Gap Between f-GANs and Wasserstein GANsCode1
Double Reinforcement Learning for Efficient and Robust Off-Policy Evaluation—0
CURL: Contrastive Unsupervised Representation Learning for Reinforcement LearningCode1
A distributional view on multi objective policy optimization—0
Learning to Navigate in Synthetically Accessible Chemical Space Using Reinforcement LearningCode1
“Other-Play” for Zero-Shot Coordination—0
Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot ControlCode1
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Benchmark Results

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