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 3551–3560 of 15113 papers

TitleStatusHype
Imitating Graph-Based Planning with Goal-Conditioned PoliciesCode1
Deceptive Reinforcement Learning in Model-Free Domains—0
DataLight: Offline Data-Driven Traffic Signal ControlCode1
A Survey of Demonstration Learning—0
Improved Sample Complexity for Reward-free Reinforcement Learning under Low-rank MDPs—0
Multi-modal reward for visual relationships-based image captioning—0
Active hypothesis testing in unknown environments using recurrent neural networks and model free reinforcement learning—0
Reinforcement Learning Friendly Vision-Language Model for MinecraftCode1
Cheap Talk Discovery and Utilization in Multi-Agent Reinforcement Learning—0
Boundary-aware Supervoxel-level Iteratively Refined Interactive 3D Image Segmentation with Multi-agent Reinforcement Learning—0
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Benchmark Results

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