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

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Showing 6101–6150 of 15113 papers

TitleStatusHype
Evolving Curricula with Regret-Based Environment Design—0
A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open ProblemsCode0
Learning in Sparse Rewards settings through Quality-Diversity algorithms—0
Follow your Nose: Using General Value Functions for Directed Exploration in Reinforcement Learning—0
Combining Reinforcement Learning and Optimal Transport for the Traveling Salesman ProblemCode0
Pareto Frontier Approximation Network (PA-Net) to Solve Bi-objective TSP—0
On the Generalization of Representations in Reinforcement LearningCode0
Affordance Learning from Play for Sample-Efficient Policy LearningCode1
Distributional Reinforcement Learning for Scheduling of Chemical Production Processes—0
Hierarchical Reinforcement Learning with AI Planning ModelsCode0
A Theory of Abstraction in Reinforcement Learning—0
Explaining a Deep Reinforcement Learning Docking Agent Using Linear Model Trees with User Adapted Visualization—0
DreamingV2: Reinforcement Learning with Discrete World Models without Reconstruction—0
Approximating a deep reinforcement learning docking agent using linear model trees—0
A Survey on Recent Advances and Challenges in Reinforcement Learning Methods for Task-Oriented Dialogue Policy Learning—0
Combining Modular Skills in Multitask LearningCode1
Avalanche RL: a Continual Reinforcement Learning LibraryCode1
Weakly Supervised Disentangled Representation for Goal-conditioned Reinforcement Learning—0
Probing the Robustness of Trained Metrics for Conversational Dialogue SystemsCode0
Provably Efficient Convergence of Primal-Dual Actor-Critic with Nonlinear Function Approximation—0
Monkey Business: Reinforcement learning meets neighborhood search for Virtual Network EmbeddingCode1
Pessimistic Q-Learning for Offline Reinforcement Learning: Towards Optimal Sample Complexity—0
Neural-Progressive Hedging: Enforcing Constraints in Reinforcement Learning with Stochastic Programming—0
Statistically Efficient Advantage Learning for Offline Reinforcement Learning in Infinite HorizonsCode0
Whittle Index based Q-Learning for Wireless Edge Caching with Linear Function Approximation—0
RL-PGO: Reinforcement Learning-based Planar Pose-Graph OptimizationCode0
Distributed Multi-Agent Reinforcement Learning Based on Graph-Induced Local Value Functions—0
Domain Knowledge-Based Automated Analog Circuit Design with Deep Reinforcement Learning—0
Exploring with Sticky Mittens: Reinforcement Learning with Expert Interventions via Option TemplatesCode0
Consolidated Adaptive T-soft Update for Deep Reinforcement Learning—0
Decision Making in Non-Stationary Environments with Policy-Augmented Monte Carlo Tree Search—0
Context-Hierarchy Inverse Reinforcement Learning—0
Learning Dynamic Mechanisms in Unknown Environments: A Reinforcement Learning Approach—0
Building a 3-Player Mahjong AI using Deep Reinforcement LearningCode1
Reachability analysis in stochastic directed graphs by reinforcement learning—0
Evolving-to-Learn Reinforcement Learning Tasks with Spiking Neural Networks—0
Learning Transferable Reward for Query Object Localization with Policy AdaptationCode0
Evolutionary Multi-Objective Reinforcement Learning Based Trajectory Control and Task Offloading in UAV-Assisted Mobile Edge Computing—0
All You Need Is Supervised Learning: From Imitation Learning to Meta-RL With Upside Down RLCode1
Quantum Deep Reinforcement Learning for Robot Navigation TasksCode0
Learning Relative Return Policies With Upside-Down Reinforcement Learning—0
Comparative analysis of machine learning methods for active flow control—0
Reinforcement Learning in Practice: Opportunities and Challenges—0
Drawing Inductor Layout with a Reinforcement Learning Agent: Method and Application for VCO Inductors—0
Blockchain Framework for Artificial Intelligence ComputationCode1
Consistent Dropout for Policy Gradient Reinforcement Learning—0
Using Deep Reinforcement Learning with Automatic Curriculum Learning for Mapless Navigation in IntralogisticsCode1
Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement LearningCode1
Reinforcement Learning from Demonstrations by Novel Interactive Expert and Application to Automatic Berthing Control Systems for Unmanned Surface Vessel—0
Training Characteristic Functions with Reinforcement Learning: XAI-methods play Connect Four—0
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Benchmark Results

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