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 4371–4380 of 15113 papers

TitleStatusHype
A comprehensive survey of research towards AI-enabled unmanned aerial systems in pre-, active-, and post-wildfire management—0
Towards an Adaptable and Generalizable Optimization Engine in Decision and Control: A Meta Reinforcement Learning Approach—0
GLIDE-RL: Grounded Language Instruction through DEmonstration in RL—0
Improving Unsupervised Hierarchical Representation with Reinforcement LearningCode0
Data Assimilation in Chaotic Systems Using Deep Reinforcement LearningCode0
Adversarially Trained Weighted Actor-Critic for Safe Offline Reinforcement Learning—0
Regularized Parameter Uncertainty for Improving Generalization in Reinforcement Learning—0
POCE: Primal Policy Optimization with Conservative Estimation for Multi-constraint Offline Reinforcement LearningCode0
Personalized Dynamic Pricing Policy for Electric Vehicles: Reinforcement learning approach—0
Training Diffusion Models Towards Diverse Image Generation with Reinforcement Learning—0
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Benchmark Results

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