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 10851–10900 of 15113 papers

TitleStatusHype
Action Space Shaping in Deep Reinforcement LearningCode1
Information State Embedding in Partially Observable Cooperative Multi-Agent Reinforcement LearningCode0
Exploration of Reinforcement Learning for Event Camera using Car-like Robots—0
Value Driven Representation for Human-in-the-Loop Reinforcement Learning—0
Safe Reinforcement Learning via Projection on a Safe Set: How to Achieve Optimality?—0
Constrained-Space Optimization and Reinforcement Learning for Complex Tasks—0
Learning Sparse Rewarded Tasks from Sub-Optimal DemonstrationsCode0
Counterfactual Multi-Agent Reinforcement Learning with Graph Convolution Communication—0
Statistically Model Checking PCTL Specifications on Markov Decision Processes via Reinforcement Learning—0
Mimicking Evolution with Reinforcement Learning—0
Augmented Q Imitation Learning (AQIL)Code0
Exploration in Action SpaceCode0
Learning to Ask Medical Questions using Reinforcement LearningCode0
Controlling Rayleigh-Bénard convection via Reinforcement Learning—0
Leverage the Average: an Analysis of KL Regularization in RL—0
Robotic Table Tennis with Model-Free Reinforcement Learning—0
Optimal Bidding Strategy without Exploration in Real-time Bidding—0
Optimising Lockdown Policies for Epidemic Control using Reinforcement LearningCode0
Straight to the Point: Fast-forwarding Videos via Reinforcement Learning Using Textual DataCode0
Ultrasound-Guided Robotic Navigation with Deep Reinforcement LearningCode1
Model-Reference Reinforcement Learning Control of Autonomous Surface Vehicles with Uncertainties—0
Agent57: Outperforming the Atari Human BenchmarkCode1
Deep reinforcement learning for large-scale epidemic controlCode1
Multi-Task Reinforcement Learning with Soft ModularizationCode1
Suphx: Mastering Mahjong with Deep Reinforcement LearningCode0
When Autonomous Systems Meet Accuracy and Transferability through AI: A Survey—0
Parallel Knowledge Transfer in Multi-Agent Reinforcement Learning—0
Sample Efficient Ensemble Learning with Catalyst.RLCode1
Obstacle Avoidance and Navigation Utilizing Reinforcement Learning with Reward ShapingCode0
Policy Teaching via Environment Poisoning: Training-time Adversarial Attacks against Reinforcement LearningCode0
Learning medical triage from clinicians using Deep Q-Learning—0
Adaptive Reward-Poisoning Attacks against Reinforcement Learning—0
AirRL: A Reinforcement Learning Approach to Urban Air Quality Inference—0
A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms—0
Modeling 3D Shapes by Reinforcement LearningCode1
Machine Learning in Asset Management—Part 2: Portfolio Construction—Weight Optimization. The Journal of Financial Data ScienceCode2
Towards Better Opioid Antagonists Using Deep Reinforcement Learning—0
ACNMP: Skill Transfer and Task Extrapolation through Learning from Demonstration and Reinforcement Learning via Representation Sharing—0
Fiber: A Platform for Efficient Development and Distributed Training for Reinforcement Learning and Population-Based MethodsCode2
An empirical investigation of the challenges of real-world reinforcement learningCode1
Learning to Play Soccer by Reinforcement and Applying Sim-to-Real to Compete in the Real World—0
Driver Modeling through Deep Reinforcement Learning and Behavioral Game Theory—0
Black-box Off-policy Estimation for Infinite-Horizon Reinforcement Learning—0
Multi-Agent Reinforcement Learning for Problems with Combined Individual and Team Reward—0
Q-Learning in Regularized Mean-field Games—0
Finite-Time Analysis of Stochastic Gradient Descent under Markov Randomness—0
Distributional Reinforcement Learning with Ensembles—0
Learning Compact Reward for Image Captioning—0
Importance of using appropriate baselines for evaluation of data-efficiency in deep reinforcement learning for Atari—0
Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement LearningCode1
Show:102550
← PrevPage 218 of 303Next →

Benchmark Results

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