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 7701–7725 of 15113 papers

TitleStatusHype
Specializing Versatile Skill Libraries using Local Mixture of ExpertsCode0
CoMPS: Continual Meta Policy Search—0
Hyper-parameter optimization based on soft actor critic and hierarchical mixture regularization—0
Application of Deep Reinforcement Learning to Payment Fraud—0
Learning to Select the Next Reasonable Mention for Entity Linking—0
Learning over All Stabilizing Nonlinear Controllers for a Partially-Observed Linear System—0
A Review for Deep Reinforcement Learning in Atari:Benchmarks, Challenges, and Solutions—0
Ambiguous Dynamic Treatment Regimes: A Reinforcement Learning Approach—0
Deep Q-Learning Market Makers in a Multi-Agent Simulated Stock Market—0
JueWu-MC: Playing Minecraft with Sample-efficient Hierarchical Reinforcement Learning—0
Attention-Based Model and Deep Reinforcement Learning for Distribution of Event Processing TasksCode0
A Transferable Approach for Partitioning Machine Learning Models on Multi-Chip-Modules—0
First-Order Regret in Reinforcement Learning with Linear Function Approximation: A Robust Estimation Approach—0
Synthetic Acute Hypotension and Sepsis Datasets Based on MIMIC-III and Published as Part of the Health Gym Project—0
QKSA: Quantum Knowledge Seeking Agent -- resource-optimized reinforcement learning using quantum process tomography—0
Model-free Nearly Optimal Control of Constrained-Input Nonlinear Systems Based on Synchronous Reinforcement Learning—0
MESA: Offline Meta-RL for Safe Adaptation and Fault Tolerance—0
Organ localisation using supervised and semi supervised approaches combining reinforcement learning with imitation learning—0
Temporal-Spatial Causal Interpretations for Vision-Based Reinforcement Learning—0
Virtual Replay CacheCode0
MDPFuzz: Testing Models Solving Markov Decision Processes—0
MDPGT: Momentum-based Decentralized Policy Gradient TrackingCode0
Flexible Option LearningCode0
Lecture Notes on Partially Known MDPs—0
Deep differentiable reinforcement learning and optimal trading—0
Show:102550
← PrevPage 309 of 605Next →

Benchmark Results

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