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 1651–1660 of 15113 papers

TitleStatusHype
An empirical investigation of the challenges of real-world reinforcement learningCode1
Multi-Agent Reinforcement Learning for Unmanned Aerial Vehicle Coordination by Multi-Critic Policy Gradient OptimizationCode1
Multi-agent Reinforcement Learning in Sequential Social DilemmasCode1
Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority InfluenceCode1
Collaborative Multi-Agent Dialogue Model Training Via Reinforcement LearningCode1
Attacking Video Recognition Models with Bullet-Screen CommentsCode1
Multi-Agent Reinforcement Learning with Temporal Logic SpecificationsCode1
Multi-Agent Task-Oriented Dialog Policy Learning with Role-Aware Reward DecompositionCode1
Attention Actor-Critic algorithm for Multi-Agent Constrained Co-operative Reinforcement LearningCode1
Making Offline RL Online: Collaborative World Models for Offline Visual Reinforcement LearningCode1
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Benchmark Results

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