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 13101–13150 of 15113 papers

TitleStatusHype
Temporal Regularization for Markov Decision ProcessCode0
Zero-Shot Transfer with Deictic Object-Oriented Representation in Reinforcement Learning—0
Transfer of Value Functions via Variational Methods—0
The Importance of Sampling inMeta-Reinforcement Learning—0
Negotiable Reinforcement Learning for Pareto Optimal Sequential Decision-Making—0
Constrained Cross-Entropy Method for Safe Reinforcement Learning—0
Data center cooling using model-predictive controlCode0
Genetic-Gated Networks for Deep Reinforcement Learning—0
Distributed Multitask Reinforcement Learning with Quadratic Convergence—0
Exponentially Weighted Imitation Learning for Batched Historical Data—0
Fighting Boredom in Recommender Systems with Linear Reinforcement Learning—0
Geometrically Coupled Monte Carlo Sampling—0
Learning Curriculum Policies for Reinforcement LearningCode0
Dynamic Measurement Scheduling for Adverse Event Forecasting using Deep RL—0
How to Organize your Deep Reinforcement Learning Agents: The Importance of Communication Topology—0
Deep Multi-Agent Reinforcement Learning with Relevance GraphsCode0
An Introduction to Deep Reinforcement LearningCode1
BlockPuzzle - A Challenge in Physical Reasoning and Generalization for Robot Learning—0
Modeling natural language emergence with integral transform theory and reinforcement learningCode0
Modulated Policy Hierarchies—0
Using Monte Carlo Tree Search as a Demonstrator within Asynchronous Deep RL—0
Flow Shape Design for Microfluidic Devices Using Deep Reinforcement Learning—0
A Structure-aware Online Learning Algorithm for Markov Decision Processes—0
Deep Reinforcement Learning for Autonomous DrivingCode0
Deep Reinforcement Learning for Time Optimal Velocity Control using Prior Knowledge—0
Trajectory-based Learning for Ball-in-Maze Games—0
Unsupervised Control Through Non-Parametric Discriminative Rewards—0
What is Interpretable? Using Machine Learning to Design Interpretable Decision-Support Systems—0
Scaling Configuration of Energy Harvesting Sensors with Reinforcement Learning—0
Quality-Aware Multimodal Saliency Detection via Deep Reinforcement Learning—0
Understanding the impact of entropy on policy optimizationCode0
Automatic Face Aging in Videos via Deep Reinforcement Learning—0
Distributed traffic light control at uncoupled intersections with real-world topology by deep reinforcement learning—0
Grammars and reinforcement learning for molecule optimizationCode0
Learning State Representations in Complex Systems with Multimodal Data—0
Genetic-Gated Networks for Deep Reinforcement—0
PNS: Population-Guided Novelty Search for Reinforcement Learning in Hard Exploration Environments—0
Environments for Lifelong Reinforcement LearningCode0
Reinforcement Learning for Uplift ModelingCode0
Reinforced Cross-Modal Matching and Self-Supervised Imitation Learning for Vision-Language Navigation—0
A Model-Based Reinforcement Learning Approach for a Rare Disease Diagnostic Task—0
Learning to Activate Relay Nodes: Deep Reinforcement Learning Approach—0
TorchProteinLibrary: A computationally efficient, differentiable representation of protein structureCode0
Model-Based Reinforcement Learning for Sepsis Treatment—0
Learning Goal Embeddings via Self-Play for Hierarchical Reinforcement LearningCode0
Integrating Reinforcement Learning to Self Training for Pulmonary Nodule Segmentation in Chest X-rays—0
Integrating Task-Motion Planning with Reinforcement Learning for Robust Decision Making in Mobile Robots—0
High-Level Strategy Selection under Partial Observability in StarCraft: Brood War—0
Urban Driving with Multi-Objective Deep Reinforcement LearningCode0
Neural Machine Translation with Adequacy-Oriented Learning—0
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Benchmark Results

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