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 14601–14650 of 15113 papers

TitleStatusHype
Collaborative Deep Reinforcement LearningCode0
Collaborative Deep Reinforcement Learning for Joint Object Search—0
Batch Policy Gradient Methods for Improving Neural Conversation Models—0
Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learningCode0
Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning—0
Autonomous Braking System via Deep Reinforcement LearningCode0
Semi-Supervised QA with Generative Domain-Adaptive Nets—0
Uncertainty-Aware Reinforcement Learning for Collision Avoidance—0
Deep Reinforcement Learning for Robotic Manipulation-The state of the art—0
Deep Reinforcement Learning for Visual Object Tracking in Videos—0
Flow Navigation by Smart Microswimmers via Reinforcement Learning—0
Expert Level control of Ramp Metering based on Multi-task Deep Reinforcement Learning—0
Reinforcement Learning Algorithm Selection—0
PathNet: Evolution Channels Gradient Descent in Super Neural NetworksCode0
Learning Light Transport the Reinforced WayCode0
Deep Reinforcement Learning: An OverviewCode0
Artificial Intelligence Approaches To UCAV Autonomy—0
Adversarial Learning for Neural Dialogue GenerationCode0
Regularizing Neural Networks by Penalizing Confident Output DistributionsCode0
Binary Matrix Guessing Problem—0
Basic protocols in quantum reinforcement learning with superconducting circuits—0
Vulnerability of Deep Reinforcement Learning to Policy Induction AttacksCode0
Near Optimal Behavior via Approximate State AbstractionCode0
Agent-Agnostic Human-in-the-Loop Reinforcement Learning—0
Scalable and Incremental Learning of Gaussian Mixture Models—0
Real-Time Bidding by Reinforcement Learning in Display AdvertisingCode0
Reinforcement Learning based Embodied Agents Modelling Human Users Through Interaction and Multi-Sensory Perception—0
Reinforcement Learning via Recurrent Convolutional Neural NetworksCode0
Toward negotiable reinforcement learning: shifting priorities in Pareto optimal sequential decision-making—0
A Review of Neural Network Based Machine Learning Approaches for Rotor Angle Stability Control—0
First-Person Activity Forecasting with Online Inverse Reinforcement Learning—0
On the function approximation error for risk-sensitive reinforcement learning—0
Non-Deterministic Policy Improvement Stabilizes Approximated Reinforcement Learning—0
A Survey of Deep Network Solutions for Learning Control in Robotics: From Reinforcement to ImitationCode0
Loss is its own Reward: Self-Supervision for Reinforcement Learning—0
Unsupervised Perceptual Rewards for Imitation Learning—0
Self-Correcting Models for Model-Based Reinforcement LearningCode0
Sample-efficient Deep Reinforcement Learning for Dialog Control—0
Reinforcement Learning Using Quantum Boltzmann Machines—0
A User Simulator for Task-Completion DialoguesCode0
Learning to predict where to look in interactive environments using deep recurrent q-learning—0
Deep Reinforcement Learning with Successor Features for Navigation across Similar Environments—0
Separation of Concerns in Reinforcement Learning—0
Response to Comment on 'Perceptual Learning Incepted by Decoded fMRI Neurofeedback Without Stimulus Presentation'; How can a decoded neurofeedback method (DecNef) lead to successful reinforcement and visual perceptual learning?—0
Incorporating Human Domain Knowledge into Large Scale Cost Function Learning—0
End-to-End Deep Reinforcement Learning for Lane Keeping Assist—0
Learning to Drive using Inverse Reinforcement Learning and Deep Q-Networks—0
Online Reinforcement Learning for Real-Time Exploration in Continuous State and Action Markov Decision Processes—0
PoseAgent: Budget-Constrained 6D Object Pose Estimation via Reinforcement Learning—0
Reinforcement Learning With Temporal Logic Rewards—0
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Benchmark Results

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