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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1841–1850 of 15113 papers

TitleStatusHype
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
Show:102550
← PrevPage 185 of 1512Next →

Benchmark Results

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