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 7426–7450 of 15113 papers

TitleStatusHype
Maneuver Decision-Making For Autonomous Air Combat Through Curriculum Learning And Reinforcement Learning With Sparse Rewards—0
MANGA: Method Agnostic Neural-policy Generalization and Adaptation—0
Manifold Embeddings for Model-Based Reinforcement Learning under Partial Observability—0
Manifold Regularization for Kernelized LSTD—0
Manipulating Reinforcement Learning: Poisoning Attacks on Cost Signals—0
Manufacturing Dispatching using Reinforcement and Transfer Learning—0
Many Agent Reinforcement Learning Under Partial Observability—0
Many Field Packet Classification with Decomposition and Reinforcement Learning—0
Many-Goals Reinforcement Learning—0
Many-Objective Reinforcement Learning for Online Testing of DNN-Enabled Systems—0
Map-based Multi-Policy Reinforcement Learning: Enhancing Adaptability of Robots by Deep Reinforcement Learning—0
MAP Inference for Bayesian Inverse Reinforcement Learning—0
MAPPER: Multi-Agent Path Planning with Evolutionary Reinforcement Learning in Mixed Dynamic Environments—0
MAPS: Multi-agent Reinforcement Learning-based Portfolio Management System—0
Marginalized Importance Sampling for Off-Environment Policy Evaluation—0
Marginalized Operators for Off-policy Reinforcement Learning—0
MarineFormer: A Spatio-Temporal Attention Model for USV Navigation in Dynamic Marine Environments—0
MarineGym: A High-Performance Reinforcement Learning Platform for Underwater Robotics—0
Market Making via Reinforcement Learning in China Commodity Market—0
Markov Chain Concentration with an Application in Reinforcement Learning—0
Markov Chain Monte Carlo Policy Optimization—0
Markov Chain Variance Estimation: A Stochastic Approximation Approach—0
Markov Cricket: Using Forward and Inverse Reinforcement Learning to Model, Predict And Optimize Batting Performance in One-Day International Cricket—0
Markov Decision Processes with Continuous Side Information—0
Markovian Interference in Experiments—0
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Benchmark Results

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