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 3021–3030 of 15113 papers

TitleStatusHype
Parameter and Computation Efficient Transfer Learning for Vision-Language Pre-trained ModelsCode0
Hundreds Guide Millions: Adaptive Offline Reinforcement Learning with Expert Guidance—0
INTAGS: Interactive Agent-Guided Simulation—0
Marginalized Importance Sampling for Off-Environment Policy Evaluation—0
Deception Game: Closing the Safety-Learning Loop in Interactive Robot Autonomy—0
Autonomous Soft Tissue Retraction Using Demonstration-Guided Reinforcement LearningCode0
Sequential Dexterity: Chaining Dexterous Policies for Long-Horizon Manipulation—0
Neurosymbolic Reinforcement Learning and Planning: A Survey—0
End-to-end Lidar-Driven Reinforcement Learning for Autonomous Racing—0
Multi-Objective Decision Transformers for Offline Reinforcement Learning—0
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Benchmark Results

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