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 9201–9225 of 15113 papers

TitleStatusHype
Reinforcement Learning For Survival, A Clinically Motivated Method For Critically Ill Patients—0
Reinforcement Learning for Systematic FX Trading—0
Reinforcement Learning for Task Specifications with Action-Constraints—0
Reinforcement Learning for Test Case Prioritization—0
Reinforcement Learning for the Beginning of Starcraft II Game—0
Reinforcement learning for the privacy preservation and manipulation of eye tracking data—0
Reinforcement Learning for Thermostatically Controlled Loads Control using Modelica and Python—0
Reinforcement Learning for the Soccer Dribbling Task—0
Reinforcement Learning for the Unit Commitment Problem—0
Reinforcement Learning for Traffic Signal Control: Comparison with Commercial Systems—0
Reinforcement learning for traffic signal control in hybrid action space—0
Reinforcement Learning for Transition-Based Mention Detection—0
Reinforcement Learning for UA V Attitude Control—0
Reinforcement Learning for UAV Autonomous Navigation, Mapping and Target Detection—0
Reinforcement Learning for UAV control with Policy and Reward Shaping—0
Reinforcement Learning for Ultrasound Image Analysis A Comprehensive Review of Advances and Applications—0
Reinforcement Learning for Variable Selection in a Branch and Bound Algorithm—0
Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control—0
Reinforcement Learning for Visual Object Detection—0
Reinforcement Learning for Volt-Var Control: A Novel Two-stage Progressive Training Strategy—0
Reinforcement Learning for Weakly Supervised Temporal Grounding of Natural Language in Untrimmed Videos—0
Reinforcement Learning Framework for Opportunistic Routing in WSNs—0
Reinforcement Learning Framework for Quantitative Trading—0
Reinforcement Learning Framework for Server Placement and Workload Allocation in Multi-Access Edge Computing—0
Reinforcement learning framework for the mechanical design of microelectronic components under multiphysics constraints—0
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Benchmark Results

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