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 4926–4950 of 15113 papers

TitleStatusHype
Reinforcement Learning for Semantic Segmentation in Indoor Scenes—0
Funnel-based Reward Shaping for Signal Temporal Logic Tasks in Reinforcement Learning—0
Reinforcement Learning for Slate-based Recommender Systems: A Tractable Decomposition and Practical Methodology—0
Reinforcement Learning for Sociohydrology—0
Reinforcement Learning for Solving the Pricing Problem in Column Generation: Applications to Vehicle Routing—0
Reinforcement Learning for Sparse-Reward Object-Interaction Tasks in a First-person Simulated 3D Environment—0
Reinforcement Learning for Standards Design—0
Reinforcement Learning for Stock Transactions—0
Reinforcement Learning for Strategic Recommendations—0
Reinforcement learning for suppression of collective activity in oscillatory ensembles—0
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
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Benchmark Results

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