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 3131–3140 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning Based Dynamic Route Planning for Minimizing Travel Time—0
Deep Reinforcement Learning based Dynamic Optimization of Bus Timetable—0
Deep Reinforcement Learning Based High-level Driving Behavior Decision-making Model in Heterogeneous Traffic—0
CtRL-Sim: Reactive and Controllable Driving Agents with Offline Reinforcement Learning—0
Deep Reinforcement Learning Based Mobile Edge Computing for Intelligent Internet of Things—0
CROPS: A Deployable Crop Management System Over All Possible State Availabilities—0
cube2net: Efficient Query-Specific Network Construction with Data Cube Organization—0
CubeTR: Learning to Solve the Rubik's Cube using Transformers—0
CubeTR: Learning to Solve The Rubiks Cube Using Transformers—0
A Survey of Demonstration Learning—0
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Benchmark Results

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