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 2281–2290 of 15113 papers

TitleStatusHype
Safety-Aware Reinforcement Learning for Electric Vehicle Charging Station Management in Distribution Network—0
Reinforcement Learning for Online Testing of Autonomous Driving Systems: a Replication and Extension Study—0
Fast Value Tracking for Deep Reinforcement Learning—0
Equivariant Ensembles and Regularization for Reinforcement Learning in Map-based Path PlanningCode2
HYDRA: A Hyper Agent for Dynamic Compositional Visual ReasoningCode1
Policy Bifurcation in Safe Reinforcement LearningCode1
Sample Complexity of Offline Distributionally Robust Linear Markov Decision Processes—0
Reinforcement Learning with Generalizable Gaussian Splatting—0
Efficient Transformer-based Hyper-parameter Optimization for Resource-constrained IoT EnvironmentsCode0
Distill2Explain: Differentiable decision trees for explainable reinforcement learning in energy application controllers—0
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Benchmark Results

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