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 3851–3875 of 15113 papers

TitleStatusHype
Offline Learning of Closed-Loop Deep Brain Stimulation Controllers for Parkinson Disease TreatmentCode0
Open Problems and Modern Solutions for Deep Reinforcement Learning—0
An Online Model-Following Projection Mechanism Using Reinforcement Learning—0
Deep Reinforcement Learning for Traffic Light Control in Intelligent Transportation Systems—0
Generalization of Deep Reinforcement Learning for Jammer-Resilient Frequency and Power Allocation—0
Online Reinforcement Learning in Non-Stationary Context-Driven EnvironmentsCode0
Developing Driving Strategies Efficiently: A Skill-Based Hierarchical Reinforcement Learning Approach—0
Reinforcement Learning in Low-Rank MDPs with Density Features—0
Reinforcement Learning with History-Dependent Dynamic Contexts—0
Deep Reinforcement Learning for Online Error Detection in Cyber-Physical Systems—0
Deep Reinforcement Learning for Cyber System Defense under Dynamic Adversarial Uncertainties—0
Learning to Optimize for Reinforcement LearningCode1
Distributional constrained reinforcement learning for supply chain optimizationCode0
Mind the Gap: Offline Policy Optimization for Imperfect RewardsCode1
Two-Stage Constrained Actor-Critic for Short Video RecommendationCode1
Reinforcing User Retention in a Billion Scale Short Video Recommender System—0
Performance Bounds for Policy-Based Average Reward Reinforcement Learning Algorithms—0
Diversity Through Exclusion (DTE): Niche Identification for Reinforcement Learning through Value-Decomposition—0
ACPO: A Policy Optimization Algorithm for Average MDPs with Constraints—0
Lower Bounds for Learning in Revealing POMDPs—0
ReLOAD: Reinforcement Learning with Optimistic Ascent-Descent for Last-Iterate Convergence in Constrained MDPs—0
Policy Expansion for Bridging Offline-to-Online Reinforcement LearningCode1
MARLIN: Soft Actor-Critic based Reinforcement Learning for Congestion Control in Real Networks—0
Multi-zone HVAC Control with Model-Based Deep Reinforcement Learning—0
Internally Rewarded Reinforcement LearningCode1
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Benchmark Results

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