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 12801–12850 of 15113 papers

TitleStatusHype
Adaptive Power System Emergency Control using Deep Reinforcement LearningCode0
Orthogonal Estimation of Wasserstein Distances—0
Successive Over Relaxation Q-Learning—0
Scene Memory Transformer for Embodied Agents in Long-Horizon Tasks—0
Skew-Fit: State-Covering Self-Supervised Reinforcement LearningCode1
Learning Self-Game-Play Agents for Combinatorial Optimization Problems—0
Improved Robustness and Safety for Autonomous Vehicle Control with Adversarial Reinforcement Learning—0
A cooperative game for automated learning of elasto-plasticity knowledge graphs and models with AI-guided experimentation—0
Learning Heuristics over Large Graphs via Deep Reinforcement LearningCode0
Improving Skin Condition Classification with a Visual Symptom Checker Trained using Reinforcement Learning—0
Pixel-Attentive Policy Gradient for Multi-Fingered Grasping in Cluttered Scenes—0
MinAtar: An Atari-Inspired Testbed for Thorough and Reproducible Reinforcement Learning ExperimentsCode0
Provably Robust Blackbox Optimization for Reinforcement Learning—0
Predicting Research Trends From ArxivCode0
RLOC: Neurobiologically Inspired Hierarchical Reinforcement Learning Algorithm for Continuous Control of Nonlinear Dynamical Systems—0
Concurrent Meta Reinforcement LearningCode0
A Hitchhiker's Guide to Statistical Comparisons of Reinforcement Learning AlgorithmsCode0
simple_rl: Reproducible Reinforcement Learning in PythonCode0
Minigo: A Case Study in Reproducing Reinforcement Learning Research—0
Continual Learning Using World Models for Pseudo-Rehearsal—0
Synthesizing Chemical Plant Operation Procedures using Knowledge, Dynamic Simulation and Deep Reinforcement Learning—0
Safety-Guided Deep Reinforcement Learning via Online Gaussian Process Estimation—0
Training in Task Space to Speed Up and Guide Reinforcement Learning—0
Using Natural Language for Reward Shaping in Reinforcement LearningCode0
Viewpoint Optimization for Autonomous Strawberry Harvesting with Deep Reinforcement LearningCode0
Online Data Poisoning Attack—0
Towards Understanding Chinese Checkers with Heuristics, Monte Carlo Tree Search, and Deep Reinforcement Learning—0
Learning Dynamics Model in Reinforcement Learning by Incorporating the Long Term Future—0
Model Primitive Hierarchical Lifelong Reinforcement LearningCode1
NoRML: No-Reward Meta Learning—0
Microscopic Traffic Simulation by Cooperative Multi-agent Deep Reinforcement Learning—0
Hybrid Actor-Critic Reinforcement Learning in Parameterized Action SpaceCode0
Asynchronous Episodic Deep Deterministic Policy Gradient: Towards Continuous Control in Computationally Complex EnvironmentsCode0
Hacking Google reCAPTCHA v3 using Reinforcement Learning—0
Budgeted Reinforcement Learning in Continuous State SpaceCode0
Straight to the point: reinforcement learning for user guidance in ultrasound—0
Discovering Options for Exploration by Minimizing Cover Time—0
Automating Predictive Modeling Process using Reinforcement Learning—0
A Regularized Approach to Sparse Optimal Policy in Reinforcement Learning—0
A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics NetworkCode1
Efficient Reinforcement Learning for StarCraft by Abstract Forward Models and Transfer LearningCode0
OmniDRL: Robust Pedestrian Detection using Deep Reinforcement Learning on Omnidirectional Cameras—0
TrojDRL: Trojan Attacks on Deep Reinforcement Learning AgentsCode0
Model-Based Reinforcement Learning for AtariCode0
Learning To Follow Directions in Street ViewCode0
Reinforcement Learning based Curriculum Optimization for Neural Machine Translation—0
Unifying Ensemble Methods for Q-learning via Social Choice Theory—0
Neural Packet Classification—0
Unsupervised Attention Mechanism across Neural Network LayersCode0
Deep Reinforcement Learning for Adaptive Caching in Hierarchical Content Delivery Networks—0
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Benchmark Results

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