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 11076–11100 of 15113 papers

TitleStatusHype
Almost Optimal Model-Free Reinforcement Learning via Reference-Advantage Decomposition—0
Learning as Reinforcement: Applying Principles of Neuroscience for More General Reinforcement Learning Agents—0
Data-Driven Learning and Load Ensemble Control—0
Attention Routing: track-assignment detailed routing using attention-based reinforcement learning—0
Tightening Exploration in Upper Confidence Reinforcement Learning—0
Self-Guided Evolution Strategies with Historical Estimated GradientsCode0
Superkernel Neural Architecture Search for Image Denoising—0
Variational Policy Propagation for Multi-agent Reinforcement Learning—0
Macro-Action-Based Deep Multi-Agent Reinforcement Learning—0
Time Adaptive Reinforcement Learning—0
Modeling Survival in model-based Reinforcement Learning—0
Show Us the Way: Learning to Manage Dialog from Demonstrations—0
F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning—0
Knowledge-guided Deep Reinforcement Learning for Interactive Recommendation—0
Deep Reinforcement Learning for Adaptive Learning Systems—0
Approximate Inverse Reinforcement Learning from Vision-based Imitation Learning—0
Goal-conditioned Batch Reinforcement Learning for Rotation Invariant Locomotion—0
Data-Driven Robust Control Using Reinforcement Learning—0
A Game Theoretic Framework for Model Based Reinforcement Learning—0
Analyzing Reinforcement Learning Benchmarks with Random Weight GuessingCode0
OptiGAN: Generative Adversarial Networks for Goal Optimized Sequence GenerationCode0
Reinforcement Learning for Safety-Critical Control under Model Uncertainty, using Control Lyapunov Functions and Control Barrier Functions—0
Reinforcement Learning in a Physics-Inspired Semi-Markov EnvironmentCode0
Safe deep reinforcement learning-based constrained optimal control scheme for active distribution networks—0
Improving Input-Output Linearizing Controllers for Bipedal Robots via Reinforcement Learning—0
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Benchmark Results

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