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 4571–4580 of 15113 papers

TitleStatusHype
A Multiagent Reinforcement Learning Algorithm with Non-linear Dynamics—0
A Multi-agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learning—0
A Multi-Agent Reinforcement Learning Method for Impression Allocation in Online Display Advertising—0
A Multi-Agent Reinforcement Learning Testbed for Cognitive Radio Applications—0
A Multi-Document Coverage Reward for RELAXed Multi-Document Summarization—0
A Multifidelity Sim-to-Real Pipeline for Verifiable and Compositional Reinforcement Learning—0
A Multimodal Learning-based Approach for Autonomous Landing of UAV—0
A MultiModal Social Robot Toward Personalized Emotion Interaction—0
A Multi-Objective Deep Reinforcement Learning Framework—0
An Abstraction-based Method to Check Multi-Agent Deep Reinforcement-Learning Behaviors—0
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Benchmark Results

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