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 7776–7800 of 15113 papers

TitleStatusHype
ReLLIE: Deep Reinforcement Learning for Customized Low-Light Image EnhancementCode1
Model Selection for Generic Reinforcement Learning—0
Shortest-Path Constrained Reinforcement Learning for Sparse Reward TasksCode1
Teaching Agents how to Map: Spatial Reasoning for Multi-Object NavigationCode1
Pessimistic Model-based Offline Reinforcement Learning under Partial Coverage—0
Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability—0
A Deep Reinforcement Learning Approach for Traffic Signal Control Optimization—0
Conservative Offline Distributional Reinforcement LearningCode1
Explore and Control with Adversarial SurpriseCode1
Reinforcement Learning based Proactive Control for Transmission Grid Resilience to Wildfire—0
The Role of Pretrained Representations for the OOD Generalization of Reinforcement Learning Agents—0
Modeling Explicit Concerning States for Reinforcement Learning in Visual DialogueCode0
Polynomial Time Reinforcement Learning in Factored State MDPs with Linear Value Functions—0
Towards Better Laplacian Representation in Reinforcement Learning with Generalized Graph DrawingCode1
R3L: Connecting Deep Reinforcement Learning to Recurrent Neural Networks for Image Denoising via Residual Recovery—0
CoBERL: Contrastive BERT for Reinforcement Learning—0
Behavior Constraining in Weight Space for Offline Reinforcement Learning—0
A Simple Reward-free Approach to Constrained Reinforcement Learning—0
Generating stable molecules using imitation and reinforcement learning—0
Out-of-Distribution Dynamics Detection: RL-Relevant Benchmarks and ResultsCode1
Backprop-Free Reinforcement Learning with Active Neural Generative CodingCode1
Distributed Deep Reinforcement Learning for Intelligent Traffic Monitoring with a Team of Aerial Robots—0
LS3: Latent Space Safe Sets for Long-Horizon Visuomotor Control of Sparse Reward Iterative TasksCode0
NVCell: Standard Cell Layout in Advanced Technology Nodes with Reinforcement Learning—0
Reinforced Hybrid Genetic Algorithm for the Traveling Salesman Problem—0
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Benchmark Results

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