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 5351–5375 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning for Multi-user Massive MIMO with Channel Aging—0
Learning a model is paramount for sample efficiency in reinforcement learning control of PDEsCode0
To Risk or Not to Risk: Learning with Risk Quantification for IoT Task Offloading in UAVs—0
Quantum algorithms applied to satellite mission planning for Earth observation—0
Regret-Based Defense in Adversarial Reinforcement LearningCode0
On Modeling Long-Term User Engagement from Stochastic Feedback—0
Universal Agent Mixtures and the Geometry of Intelligence—0
A Lifetime Extended Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles via Self-Learning Fuzzy Reinforcement Learning—0
Maneuver Decision-Making For Autonomous Air Combat Through Curriculum Learning And Reinforcement Learning With Sparse Rewards—0
Robust Representation Learning by Clustering with Bisimulation Metrics for Visual Reinforcement Learning with DistractionsCode0
ReMIX: Regret Minimization for Monotonic Value Function Factorization in Multiagent Reinforcement Learning—0
Cross-domain Random Pre-training with Prototypes for Reinforcement LearningCode0
Low Entropy Communication in Multi-Agent Reinforcement Learning—0
A Survey on Causal Reinforcement Learning—0
Combining Reconstruction and Contrastive Methods for Multimodal Representations in RLCode0
Towards Minimax Optimality of Model-based Robust Reinforcement Learning—0
Scaling Goal-based Exploration via Pruning Proto-goals—0
Data Quality-aware Mixed-precision Quantization via Hybrid Reinforcement Learning—0
Learning Complex Teamwork Tasks Using a Given Sub-task DecompositionCode0
CLARE: Conservative Model-Based Reward Learning for Offline Inverse Reinforcement Learning—0
Equivariant MuZero—0
An Investigation into Pre-Training Object-Centric Representations for Reinforcement Learning—0
A Scale-Independent Multi-Objective Reinforcement Learning with Convergence Analysis—0
Learning Graph-Enhanced Commander-Executor for Multi-Agent NavigationCode0
Efficient Planning in Combinatorial Action Spaces with Applications to Cooperative Multi-Agent Reinforcement Learning—0
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Benchmark Results

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