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 671–680 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning at the Edge of the Statistical PrecipiceCode1
Deep Reinforcement Learning based Evasion Generative Adversarial Network for Botnet DetectionCode1
Deep Policies for Online Bipartite Matching: A Reinforcement Learning ApproachCode1
Deep Multi-agent Reinforcement Learning for Highway On-Ramp Merging in Mixed TrafficCode1
Faster Deep Reinforcement Learning with Slower Online NetworkCode1
A Text-based Deep Reinforcement Learning Framework for Interactive RecommendationCode1
A Sustainable Ecosystem through Emergent Cooperation in Multi-Agent Reinforcement LearningCode1
Adversarial Search and Tracking with Multiagent Reinforcement Learning in Sparsely Observable EnvironmentCode1
Asynchronous Methods for Deep Reinforcement LearningCode1
Attractive or Faithful? Popularity-Reinforced Learning for Inspired Headline GenerationCode1
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Benchmark Results

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