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 11051–11100 of 15113 papers

TitleStatusHype
Molecular Design in Synthetically Accessible Chemical Space via Deep Reinforcement Learning—0
Meta-Reinforcement Learning for Robotic Industrial Insertion Tasks—0
The Immersion of Directed Multi-graphs in Embedding Fields. Generalisations—0
Improving Sample Efficiency and Multi-Agent Communication in RL-based Train Rescheduling—0
Age-Aware Status Update Control for Energy Harvesting IoT Sensors via Reinforcement Learning—0
Can We Learn Heuristics For Graphical Model Inference Using Reinforcement Learning?—0
Adaptive model selection in photonic reservoir computing by reinforcement learning—0
Evolving Inborn Knowledge For Fast Adaptation in Dynamic POMDP ProblemsCode0
The Ingredients of Real-World Robotic Reinforcement Learning—0
Reinforcement Learning Generalization with Surprise MinimizationCode0
A State Aggregation Approach for Solving Knapsack Problem with Deep Reinforcement Learning—0
Automatic low-bit hybrid quantization of neural networks through meta learning—0
PBCS : Efficient Exploration and Exploitation Using a Synergy between Reinforcement Learning and Motion Planning—0
Divide-and-Conquer Monte Carlo Tree Search For Goal-Directed Planning—0
Guiding Robot Exploration in Reinforcement Learning via Automated Planning—0
Cooperative Perception with Deep Reinforcement Learning for Connected Vehicles—0
Learning Dialog Policies from Weak Demonstrations—0
Correct Me If You Can: Learning from Error Corrections and MarkingsCode0
Flexible and Efficient Long-Range Planning Through Curious Exploration—0
AutoEG: Automated Experience Grafting for Off-Policy Deep Reinforcement Learning—0
Mean-Variance Policy Iteration for Risk-Averse Reinforcement Learning—0
Sequential Anomaly Detection using Inverse Reinforcement Learning—0
Reinforcement Learning to Optimize the Logistics Distribution Routes of Unmanned Aerial Vehicle—0
SIBRE: Self Improvement Based REwards for Adaptive Feedback in Reinforcement Learning—0
Never Stop Learning: The Effectiveness of Fine-Tuning in Robotic Reinforcement Learning—0
Almost Optimal Model-Free Reinforcement Learning via Reference-Advantage Decomposition—0
Learning as Reinforcement: Applying Principles of Neuroscience for More General Reinforcement Learning Agents—0
Data-Driven Learning and Load Ensemble Control—0
Attention Routing: track-assignment detailed routing using attention-based reinforcement learning—0
Tightening Exploration in Upper Confidence Reinforcement Learning—0
Self-Guided Evolution Strategies with Historical Estimated GradientsCode0
Superkernel Neural Architecture Search for Image Denoising—0
Variational Policy Propagation for Multi-agent Reinforcement Learning—0
Macro-Action-Based Deep Multi-Agent Reinforcement Learning—0
Time Adaptive Reinforcement Learning—0
Modeling Survival in model-based Reinforcement Learning—0
Show Us the Way: Learning to Manage Dialog from Demonstrations—0
F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning—0
Knowledge-guided Deep Reinforcement Learning for Interactive Recommendation—0
Deep Reinforcement Learning for Adaptive Learning Systems—0
Approximate Inverse Reinforcement Learning from Vision-based Imitation Learning—0
Goal-conditioned Batch Reinforcement Learning for Rotation Invariant Locomotion—0
Data-Driven Robust Control Using Reinforcement Learning—0
A Game Theoretic Framework for Model Based Reinforcement Learning—0
Analyzing Reinforcement Learning Benchmarks with Random Weight GuessingCode0
OptiGAN: Generative Adversarial Networks for Goal Optimized Sequence GenerationCode0
Reinforcement Learning for Safety-Critical Control under Model Uncertainty, using Control Lyapunov Functions and Control Barrier Functions—0
Reinforcement Learning in a Physics-Inspired Semi-Markov EnvironmentCode0
Safe deep reinforcement learning-based constrained optimal control scheme for active distribution networks—0
Improving Input-Output Linearizing Controllers for Bipedal Robots via Reinforcement Learning—0
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Benchmark Results

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