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 2271–2280 of 15113 papers

TitleStatusHype
Behavior Priors for Efficient Reinforcement Learning—0
Analytically Tractable Bayesian Deep Q-Learning—0
Adaptive Transit Signal Priority based on Deep Reinforcement Learning and Connected Vehicles in a Traffic Microsimulation Environment—0
A comprehensive survey of research towards AI-enabled unmanned aerial systems in pre-, active-, and post-wildfire management—0
Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics—0
Behavior Planning at Urban Intersections through Hierarchical Reinforcement Learning—0
Behavior-Guided Reinforcement Learning—0
Analysis on Riemann Hypothesis with Cross Entropy Optimization and Reasoning—0
A Boosting Approach to Reinforcement Learning—0
Behavior Constraining in Weight Space for Offline Reinforcement Learning—0
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Benchmark Results

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