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

Showing 20012025 of 15113 papers

TitleStatusHype
Intelligent Electric Vehicle Charging Recommendation Based on Multi-Agent Reinforcement LearningCode1
Storehouse: a Reinforcement Learning Environment for Optimizing Warehouse ManagementCode1
Bingham Policy Parameterization for 3D Rotations in Reinforcement LearningCode1
Strategically Efficient Exploration in Competitive Multi-agent Reinforcement LearningCode1
An Optimistic Perspective on Offline Reinforcement LearningCode1
Learning to Paint With Model-based Deep Reinforcement LearningCode1
IGLU Gridworld: Simple and Fast Environment for Embodied Dialog AgentsCode1
ImagineBench: Evaluating Reinforcement Learning with Large Language Model RolloutsCode1
IG-RL: Inductive Graph Reinforcement Learning for Massive-Scale Traffic Signal ControlCode1
Succinct and Robust Multi-Agent Communication With Temporal Message ControlCode1
Intelligent Reflecting Surface Configurations for Smart Radio Using Deep Reinforcement LearningCode1
BIMRL: Brain Inspired Meta Reinforcement LearningCode1
Image Classification by Reinforcement Learning with Two-State Q-LearningCode1
Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from PixelsCode1
BayesSimIG: Scalable Parameter Inference for Adaptive Domain Randomization with IsaacGymCode1
Analysis of diversity-accuracy tradeoff in image captioningCode1
Integrating Saliency Ranking and Reinforcement Learning for Enhanced Object DetectionCode1
Imitating Graph-Based Planning with Goal-Conditioned PoliciesCode1
BCORLE(): An Offline Reinforcement Learning and Evaluation Framework for Coupons Allocation in E-commerce MarketCode1
Improving Computational Efficiency in Visual Reinforcement Learning via Stored EmbeddingsCode1
Intelligent Trading Systems: A Sentiment-Aware Reinforcement Learning ApproachCode1
IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner ArchitecturesCode1
BEAR: Physics-Principled Building Environment for Control and Reinforcement LearningCode1
A Composable Specification Language for Reinforcement Learning TasksCode1
Integrating Deep Reinforcement Learning Networks with Health System SimulationsCode1
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Benchmark Results

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