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 6801–6850 of 15113 papers

TitleStatusHype
GraMeR: Graph Meta Reinforcement Learning for Multi-Objective Influence Maximization—0
Designing Rewards for Fast Learning—0
Efficient Reward Poisoning Attacks on Online Deep Reinforcement LearningCode0
Learning Open Domain Multi-hop Search Using Reinforcement Learning—0
A Simulation Environment and Reinforcement Learning Method for Waste Reduction—0
Learning Security Strategies through Game Play and Optimal Stopping—0
Frustratingly Easy Regularization on Representation Can Boost Deep Reinforcement Learning—0
Survival Analysis on Structured Data using Deep Reinforcement Learning—0
Multi-Source Transfer Learning for Deep Model-Based Reinforcement Learning—0
Non-Markovian policies occupancy measures—0
Off-Beat Multi-Agent Reinforcement Learning—0
Tutorial on Course-of-Action (COA) Attack Search Methods in Computer Networks—0
Why So Pessimistic? Estimating Uncertainties for Offline RL through Ensembles, and Why Their Independence Matters—0
Provably Sample-Efficient RL with Side Information about Latent Dynamics—0
Feudal Multi-Agent Reinforcement Learning with Adaptive Network Partition for Traffic Signal Control—0
IGLU 2022: Interactive Grounded Language Understanding in a Collaborative Environment at NeurIPS 2022Code0
KL-Entropy-Regularized RL with a Generative Model is Minimax Optimal—0
GALOIS: Boosting Deep Reinforcement Learning via Generalizable Logic Synthesis—0
Double Deep Q Networks for Sensor Management in Space Situational Awareness—0
Deep Reinforcement Learning for Distributed and Uncoordinated Cognitive Radios Resource Allocation—0
Does DQN Learn?—0
Dynamic Network Reconfiguration for Entropy Maximization using Deep Reinforcement LearningCode0
Constrained Reinforcement Learning for Short Video Recommendation—0
A Fair Federated Learning Framework With Reinforcement Learning—0
Embed to Control Partially Observed Systems: Representation Learning with Provable Sample Efficiency—0
RACE: A Reinforcement Learning Framework for Improved Adaptive Control of NoC Channel Buffers—0
Unsupervised Reinforcement Adaptation for Class-Imbalanced Text ClassificationCode0
SFP: State-free Priors for Exploration in Off-Policy Reinforcement Learning—0
Reinforcement Learning Approach for Mapping Applications to Dataflow-Based Coarse-Grained Reconfigurable ArrayCode0
Physics-Guided Hierarchical Reward Mechanism for Learning-Based Robotic Grasping—0
Pessimism in the Face of Confounders: Provably Efficient Offline Reinforcement Learning in Partially Observable Markov Decision Processes—0
MAVIPER: Learning Decision Tree Policies for Interpretable Multi-Agent Reinforcement Learning—0
Transportation-Inequalities, Lyapunov Stability and Sampling for Dynamical Systems on Continuous State Space—0
Trust-based Consensus in Multi-Agent Reinforcement Learning Systems—0
Robust Reinforcement Learning on Graphs for Logistics optimization—0
Near-Optimal Goal-Oriented Reinforcement Learning in Non-Stationary Environments—0
Tiered Reinforcement Learning: Pessimism in the Face of Uncertainty and Constant RegretCode0
Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function—0
Skill Machines: Temporal Logic Skill Composition in Reinforcement LearningCode0
Learning in Mean Field Games: A Survey—0
Impartial Games: A Challenge for Reinforcement LearningCode0
Fast Inference and Transfer of Compositional Task Structures for Few-shot Task Generalization—0
An Experimental Comparison Between Temporal Difference and Residual Gradient with Neural Network Approximation—0
Learning to Query Internet Text for Informing Reinforcement Learning Agents—0
Graph Convolutional Reinforcement Learning for Collaborative Queuing Agents—0
Distributional Hamilton-Jacobi-Bellman Equations for Continuous-Time Reinforcement Learning—0
Concurrent Credit Assignment for Data-efficient Reinforcement LearningCode0
Deep Reinforcement Learning for Multi-class Imbalanced TrainingCode0
Learning to Drive Using Sparse Imitation Reinforcement Learning—0
Meta Policy Learning for Cold-Start Conversational RecommendationCode0
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Benchmark Results

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