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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2576–2600 of 15113 papers

TitleStatusHype
Adaptive Discounting of Training Time Attacks—0
A unified uncertainty-aware exploration: Combining epistemic and aleatory uncertainty—0
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
A Robust Quantile Huber Loss With Interpretable Parameter Adjustment In Distributional Reinforcement LearningCode0
GLIDE-RL: Grounded Language Instruction through DEmonstration in RL—0
Training Diffusion Models Towards Diverse Image Generation with Reinforcement Learning—0
Improving Unsupervised Hierarchical Representation with Reinforcement LearningCode0
POCE: Primal Policy Optimization with Conservative Estimation for Multi-constraint Offline Reinforcement LearningCode0
DMR: Decomposed Multi-Modality Representations for Frames and Events Fusion in Visual Reinforcement LearningCode1
Regularized Parameter Uncertainty for Improving Generalization in Reinforcement Learning—0
Personalized Dynamic Pricing Policy for Electric Vehicles: Reinforcement learning approach—0
Adversarially Trained Weighted Actor-Critic for Safe Offline Reinforcement Learning—0
Data Assimilation in Chaotic Systems Using Deep Reinforcement LearningCode0
Tight Finite Time Bounds of Two-Time-Scale Linear Stochastic Approximation with Markovian Noise—0
Online Symbolic Music Alignment with Offline Reinforcement LearningCode1
Laboratory Experiments of Model-based Reinforcement Learning for Adaptive Optics ControlCode0
Causal State Distillation for Explainable Reinforcement LearningCode0
Design Space Exploration of Approximate Computing Techniques with a Reinforcement Learning Approach—0
Resilient Constrained Reinforcement Learning—0
Rethinking Model-based, Policy-based, and Value-based Reinforcement Learning via the Lens of Representation ComplexityCode0
Generalizable Visual Reinforcement Learning with Segment Anything ModelCode1
Beyond PID Controllers: PPO with Neuralized PID Policy for Proton Beam Intensity Control in Mu2e—0
RL-MPCA: A Reinforcement Learning Based Multi-Phase Computation Allocation Approach for Recommender Systems—0
Conversational Question Answering with Reformulations over Knowledge Graph—0
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Benchmark Results

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