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 4201–4210 of 15113 papers

TitleStatusHype
Misspecification in Inverse Reinforcement Learning—0
PrefRec: Recommender Systems with Human Preferences for Reinforcing Long-term User EngagementCode1
State Space Closure: Revisiting Endless Online Level Generation via Reinforcement LearningCode0
What is the Solution for State-Adversarial Multi-Agent Reinforcement Learning?Code0
Active Classification of Moving Targets with Learned Control Policies—0
Efficient Learning of Voltage Control Strategies via Model-based Deep Reinforcement Learning—0
A Learned Simulation Environment to Model Plant Growth in Indoor Farming—0
A Novel Deep Reinforcement Learning Based Automated Stock Trading System Using Cascaded LSTM Networks—0
A Hierarchical Deep Reinforcement Learning Framework for 6-DOF UCAV Air-to-Air Combat—0
Learning Physically Realizable Skills for Online Packing of General 3D ShapesCode2
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Benchmark Results

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