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 5801–5850 of 15113 papers

TitleStatusHype
HyperNCA: Growing Developmental Networks with Neural Cellular AutomataCode1
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
Reward Reports for Reinforcement LearningCode1
TASAC: a twin-actor reinforcement learning framework with stochastic policy for batch process control—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
6GAN: IPv6 Multi-Pattern Target Generation via Generative Adversarial Nets with Reinforcement LearningCode1
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
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
Understanding and Preventing Capacity Loss in Reinforcement Learning—0
SAAC: Safe Reinforcement Learning as an Adversarial Game of Actor-Critics—0
Reinforcement Learning with Intrinsic Affinity for Personalized Prosperity Management—0
Mingling Foresight with Imagination: Model-Based Cooperative Multi-Agent Reinforcement Learning—0
A Reinforcement Learning-based Volt-VAR Control Dataset and Testing EnvironmentCode1
A Hierarchical Bayesian Approach to Inverse Reinforcement Learning with Symbolic Reward Machines—0
Comparing Deep Reinforcement Learning Algorithms in Two-Echelon Supply ChainsCode1
Federated Learning for Distributed Energy-Efficient Resource Allocation—0
Network Topology Optimization via Deep Reinforcement Learning—0
When Is Partially Observable Reinforcement Learning Not Scary?—0
COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction EstimationCode1
Optimizing Tensor Network Contraction Using Reinforcement Learning—0
Training and Evaluation of Deep Policies using Reinforcement Learning and Generative Models—0
INFOrmation Prioritization through EmPOWERment in Visual Model-Based RL—0
FedKL: Tackling Data Heterogeneity in Federated Reinforcement Learning by Penalizing KL DivergenceCode1
CHAI: A CHatbot AI for Task-Oriented Dialogue with Offline Reinforcement LearningCode2
Learning to Transfer Role Assignment Across Team Sizes—0
Probabilistic Charging Power Forecast of EVCS: Reinforcement Learning Assisted Deep Learning Approach—0
Towards Comprehensive Testing on the Robustness of Cooperative Multi-agent Reinforcement Learning—0
Efficient Reinforcement Learning for Unsupervised Controlled Text Generation—0
Efficient Bayesian Policy Reuse with a Scalable Observation Model in Deep Reinforcement Learning—0
Safe Reinforcement Learning Using Black-Box Reachability AnalysisCode0
TabNAS: Rejection Sampling for Neural Architecture Search on Tabular DatasetsCode0
The Importance of Credo in Multiagent Learning—0
Understanding Game-Playing Agents with Natural Language AnnotationsCode0
CryoRL: Reinforcement Learning Enables Efficient Cryo-EM Data Collection—0
Efficient and practical quantum compiler towards multi-qubit systems with deep reinforcement learning—0
Methodical Advice Collection and Reuse in Deep Reinforcement Learning—0
Reinforcement Learning Policy Recommendation for Interbank Network Stability—0
Reinforcement learning on graphs: A surveyCode1
Local Feature Swapping for Generalization in Reinforcement Learning—0
Self-critical Sequence Training for Automatic Speech Recognition—0
Modularity benefits reinforcement learning agents with competing homeostatic drives—0
Flexible Multiple-Objective Reinforcement Learning for Chip Placement—0
Improving generalization to new environments and removing catastrophic forgetting in Reinforcement Learning by using an eco-system of agents—0
Can Question Rewriting Help Conversational Question Answering?Code1
Smart Interference Management xApp using Deep Reinforcement Learning—0
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Benchmark Results

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