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 1826–1850 of 15113 papers

TitleStatusHype
Robust Reinforcement Learning using Adversarial PopulationsCode1
Reinforced Epidemic Control: Saving Both Lives and EconomyCode1
PixL2R: Guiding Reinforcement Learning Using Natural Language by Mapping Pixels to RewardsCode1
Munchausen Reinforcement LearningCode1
Multi-Step Reinforcement Learning for Single Image Super-ResolutionCode1
Lifelong Incremental Reinforcement Learning with Online Bayesian InferenceCode1
Combining Deep Reinforcement Learning and Search for Imperfect-Information GamesCode1
Maximum Mutation Reinforcement Learning for Scalable ControlCode1
Autonomous Exploration Under Uncertainty via Deep Reinforcement Learning on GraphsCode1
BabyAI 1.1Code1
Value-Decomposition Multi-Agent Actor-CriticsCode1
Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued PoliciesCode1
Monte-Carlo Tree Search as Regularized Policy OptimizationCode1
Distributional Reinforcement Learning via Moment MatchingCode1
Integrating Deep Reinforcement Learning Networks with Health System SimulationsCode1
Active MR k-space Sampling with Reinforcement LearningCode1
Battlesnake Challenge: A Multi-agent Reinforcement Learning Playground with Human-in-the-loopCode1
Discovering Reinforcement Learning AlgorithmsCode1
Off-Policy Reinforcement Learning for Efficient and Effective GAN Architecture SearchCode1
WordCraft: An Environment for Benchmarking Commonsense AgentsCode1
Provably Good Batch Reinforcement Learning Without Great ExplorationCode1
Weighing Counts: Sequential Crowd Counting by Reinforcement LearningCode1
Developmental Reinforcement Learning of Control Policy of a Quadcopter UAV with Thrust Vectoring RotorsCode1
Learning Robust State Abstractions for Hidden-Parameter Block MDPsCode1
Implicit Distributional Reinforcement LearningCode1
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Benchmark Results

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