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 451–475 of 15113 papers

TitleStatusHype
ACN-Sim: An Open-Source Simulator for Data-Driven Electric Vehicle Charging ResearchCode1
Deceptive Path Planning via Reinforcement Learning with Graph Neural NetworksCode1
CTDS: Centralized Teacher with Decentralized Student for Multi-Agent Reinforcement LearningCode1
DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character SkillsCode1
A coevolutionary approach to deep multi-agent reinforcement learningCode1
Asynchronous Methods for Deep Reinforcement LearningCode1
Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative ExplorationCode1
A Traffic Light Dynamic Control Algorithm with Deep Reinforcement Learning Based on GNN PredictionCode1
CURL: Contrastive Unsupervised Representations for Reinforcement LearningCode1
CurricuLLM: Automatic Task Curricula Design for Learning Complex Robot Skills using Large Language ModelsCode1
A Text-based Deep Reinforcement Learning Framework for Interactive RecommendationCode1
Aspect Sentiment Triplet Extraction Using Reinforcement LearningCode1
A Benchmark Environment for Offline Reinforcement Learning in Racing GamesCode1
Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority InfluenceCode1
CROP: Conservative Reward for Model-based Offline Policy OptimizationCode1
Attacking Video Recognition Models with Bullet-Screen CommentsCode1
A Benchmark Environment Motivated by Industrial Control ProblemsCode1
DARTS: Differentiable Architecture SearchCode1
Attractive or Faithful? Popularity-Reinforced Learning for Inspired Headline GenerationCode1
CropGym: a Reinforcement Learning Environment for Crop ManagementCode1
Ask Your Humans: Using Human Instructions to Improve Generalization in Reinforcement LearningCode1
A Closer Look at Advantage-Filtered Behavioral Cloning in High-Noise DatasetsCode1
Critic Regularized RegressionCode1
A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum GamesCode1
Cross-Domain Policy Adaptation by Capturing Representation MismatchCode1
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Benchmark Results

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