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 11151–11200 of 15113 papers

TitleStatusHype
Safe Reinforcement Learning via Projection on a Safe Set: How to Achieve Optimality?—0
Statistically Model Checking PCTL Specifications on Markov Decision Processes via Reinforcement Learning—0
Counterfactual Multi-Agent Reinforcement Learning with Graph Convolution Communication—0
Learning Sparse Rewarded Tasks from Sub-Optimal DemonstrationsCode0
Constrained-Space Optimization and Reinforcement Learning for Complex Tasks—0
Controlling Rayleigh-Bénard convection via Reinforcement Learning—0
Learning to Ask Medical Questions using Reinforcement LearningCode0
Leverage the Average: an Analysis of KL Regularization in RL—0
Augmented Q Imitation Learning (AQIL)Code0
Exploration in Action SpaceCode0
Straight to the Point: Fast-forwarding Videos via Reinforcement Learning Using Textual DataCode0
Mimicking Evolution with Reinforcement Learning—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
Suphx: Mastering Mahjong with Deep Reinforcement LearningCode0
Model-Reference Reinforcement Learning Control of Autonomous Surface Vehicles with Uncertainties—0
Parallel Knowledge Transfer in Multi-Agent Reinforcement Learning—0
When Autonomous Systems Meet Accuracy and Transferability through AI: A Survey—0
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
AirRL: A Reinforcement Learning Approach to Urban Air Quality Inference—0
Adaptive Reward-Poisoning Attacks against Reinforcement Learning—0
A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms—0
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
Learning to Play Soccer by Reinforcement and Applying Sim-to-Real to Compete in the Real World—0
Black-box Off-policy Estimation for Infinite-Horizon Reinforcement Learning—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
Driver Modeling through Deep Reinforcement Learning and Behavioral Game Theory—0
Q-Learning in Regularized Mean-field Games—0
Multi-Agent Reinforcement Learning for Problems with Combined Individual and Team Reward—0
Incorporating Relational Background Knowledge into Reinforcement Learning via Differentiable Inductive Logic Programming—0
Importance of using appropriate baselines for evaluation of data-efficiency in deep reinforcement learning for Atari—0
Learning to Walk: Spike Based Reinforcement Learning for Hexapod Robot Central Pattern Generation—0
Reinforcement Learning in Economics and Finance—0
Autonomous UAV Navigation: A DDPG-based Deep Reinforcement Learning Approach—0
Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics—0
Distributed Reinforcement Learning for Cooperative Multi-Robot Object Manipulation—0
Deep Reinforcement Learning with Robust and Smooth Policy—0
Deep Sets for Generalization in RL—0
Deep Reinforcement Learning with Weighted Q-Learning—0
Deep Constrained Q-learning—0
Safe Reinforcement Learning of Control-Affine Systems with Vertex NetworksCode0
Towards Cognitive Routing based on Deep Reinforcement Learning—0
Reinforcement learning enabled cooperative spectrum sensing in cognitive radio networks—0
Exchangeable Input Representations for Reinforcement Learning—0
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Benchmark Results

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