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 13511360 of 15113 papers

TitleStatusHype
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement LearningCode1
Exploration by Random Network DistillationCode1
Exploration via Elliptical Episodic BonusesCode1
Exploration via Planning for Information about the Optimal TrajectoryCode1
Communicative Reinforcement Learning Agents for Landmark Detection in Brain ImagesCode1
Collaborative Multi-Agent Dialogue Model Training Via Reinforcement LearningCode1
Making Offline RL Online: Collaborative World Models for Offline Visual Reinforcement LearningCode1
Expression might be enough: representing pressure and demand for reinforcement learning based traffic signal controlCode1
Coevolving with the Other You: Fine-Tuning LLM with Sequential Cooperative Multi-Agent Reinforcement LearningCode1
Co-designing Intelligent Control of Building HVACs and MicrogridsCode1
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Benchmark Results

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