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 7001–7050 of 15113 papers

TitleStatusHype
Towards Evaluating Adaptivity of Model-Based Reinforcement Learning MethodsCode0
Multi-objective Pointer Network for Combinatorial OptimizationCode0
Deep Reinforcement Learning for Online Routing of Unmanned Aerial Vehicles with Wireless Power Transfer—0
Deep Reinforcement Learning for Orienteering Problems Based on Decomposition—0
Deep Reinforcement Learning-based Radio Resource Allocation and Beam Management under Location Uncertainty in 5G mmWave Networks—0
Graph Neural Network based Agent in Google Research Football—0
Finite-Time Analysis of Temporal Difference Learning: Discrete-Time Linear System Perspective—0
TASAC: a twin-actor reinforcement learning framework with stochastic policy for batch process control—0
Revisiting Gaussian mixture critics in off-policy reinforcement learning: a sample-based approach—0
Resilient robot teams: a review integrating decentralised control, change-detection, and learning—0
Optimizing Nitrogen Management with Deep Reinforcement Learning and Crop Simulations—0
Learning how to Interact with a Complex Interface using Hierarchical Reinforcement Learning—0
A Hierarchical Bayesian Approach to Inverse Reinforcement Learning with Symbolic Reward Machines—0
Federated Learning for Distributed Energy-Efficient Resource Allocation—0
Joint Learning of Reward Machines and Policies in Environments with Partially Known Semantics—0
Reinforcement Learning from Partial Observation: Linear Function Approximation with Provable Sample Efficiency—0
Reinforcement Learning with Intrinsic Affinity for Personalized Prosperity Management—0
Mingling Foresight with Imagination: Model-Based Cooperative Multi-Agent Reinforcement Learning—0
SAAC: Safe Reinforcement Learning as an Adversarial Game of Actor-Critics—0
Understanding and Preventing Capacity Loss in Reinforcement Learning—0
Network Topology Optimization via Deep Reinforcement Learning—0
When Is Partially Observable Reinforcement Learning Not Scary?—0
Training and Evaluation of Deep Policies using Reinforcement Learning and Generative Models—0
Optimizing Tensor Network Contraction Using Reinforcement Learning—0
INFOrmation Prioritization through EmPOWERment in Visual Model-Based RL—0
Learning to Transfer Role Assignment Across Team Sizes—0
Towards Comprehensive Testing on the Robustness of Cooperative Multi-agent Reinforcement Learning—0
Probabilistic Charging Power Forecast of EVCS: Reinforcement Learning Assisted Deep Learning Approach—0
Efficient Reinforcement Learning for Unsupervised Controlled Text Generation—0
Efficient Bayesian Policy Reuse with a Scalable Observation Model in Deep Reinforcement Learning—0
CryoRL: Reinforcement Learning Enables Efficient Cryo-EM Data Collection—0
TabNAS: Rejection Sampling for Neural Architecture Search on Tabular DatasetsCode0
The Importance of Credo in Multiagent Learning—0
Safe Reinforcement Learning Using Black-Box Reachability AnalysisCode0
Understanding Game-Playing Agents with Natural Language AnnotationsCode0
Methodical Advice Collection and Reuse in Deep Reinforcement Learning—0
Reinforcement Learning Policy Recommendation for Interbank Network Stability—0
Efficient and practical quantum compiler towards multi-qubit systems with deep reinforcement learning—0
Flexible Multiple-Objective Reinforcement Learning for Chip Placement—0
Local Feature Swapping for Generalization in Reinforcement Learning—0
Improving generalization to new environments and removing catastrophic forgetting in Reinforcement Learning by using an eco-system of agents—0
Self-critical Sequence Training for Automatic Speech Recognition—0
Modularity benefits reinforcement learning agents with competing homeostatic drives—0
RL-CoSeg : A Novel Image Co-Segmentation Algorithm with Deep Reinforcement Learning—0
When Should We Prefer Offline Reinforcement Learning Over Behavioral Cloning?—0
Forgetting and Imbalance in Robot Lifelong Learning with Off-policy Data—0
Smart Interference Management xApp using Deep Reinforcement Learning—0
A Reinforcement Learning Approach for Electric Vehicle Routing Problem with Vehicle-to-Grid Supply—0
AdaTest:Reinforcement Learning and Adaptive Sampling for On-chip Hardware Trojan Detection—0
An Analysis of Discretization Methods for Communication Learning with Multi-Agent Reinforcement Learning—0
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Benchmark Results

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