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 5401–5425 of 15113 papers

TitleStatusHype
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
Developing Driving Strategies Efficiently: A Skill-Based Hierarchical Reinforcement Learning Approach—0
Online Reinforcement Learning in Non-Stationary Context-Driven EnvironmentsCode0
Reinforcement Learning in Low-Rank MDPs with Density Features—0
Reinforcement Learning with History-Dependent Dynamic Contexts—0
Reinforcing User Retention in a Billion Scale Short Video Recommender System—0
Deep Reinforcement Learning for Cyber System Defense under Dynamic Adversarial Uncertainties—0
Deep Reinforcement Learning for Online Error Detection in Cyber-Physical Systems—0
Distributional constrained reinforcement learning for supply chain optimizationCode0
ACPO: A Policy Optimization Algorithm for Average MDPs with Constraints—0
Lower Bounds for Learning in Revealing POMDPs—0
Diversity Through Exclusion (DTE): Niche Identification for Reinforcement Learning through Value-Decomposition—0
MARLIN: Soft Actor-Critic based Reinforcement Learning for Congestion Control in Real Networks—0
ReLOAD: Reinforcement Learning with Optimistic Ascent-Descent for Last-Iterate Convergence in Constrained MDPs—0
Performance Bounds for Policy-Based Average Reward Reinforcement Learning Algorithms—0
Sample Complexity of Kernel-Based Q-Learning—0
Multi-zone HVAC Control with Model-Based Deep Reinforcement Learning—0
Selective Uncertainty Propagation in Offline RL—0
Bridging Physics-Informed Neural Networks with Reinforcement Learning: Hamilton-Jacobi-Bellman Proximal Policy Optimization (HJBPPO)—0
QMP: Q-switch Mixture of Policies for Multi-Task Behavior Sharing—0
Collaborating with language models for embodied reasoning—0
Combining Deep Reinforcement Learning and Search with Generative Models for Game-Theoretic Opponent Modeling—0
A Reinforcement Learning Framework for Dynamic Mediation AnalysisCode0
Few-Shot Image-to-Semantics Translation for Policy Transfer in Reinforcement LearningCode0
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Benchmark Results

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