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 4701–4725 of 15113 papers

TitleStatusHype
An Invitation to Deep Reinforcement Learning—0
An L^2 Analysis of Reinforcement Learning in High Dimensions with Kernel and Neural Network Approximation—0
An MRP Formulation for Supervised Learning: Generalized Temporal Difference Learning Models—0
Annotating Motion Primitives for Simplifying Action Search in Reinforcement Learning—0
An ocular biomechanics environment for reinforcement learning—0
An Offline Adaptation Framework for Constrained Multi-Objective Reinforcement Learning—0
An Offline Deep Reinforcement Learning for Maintenance Decision-Making—0
An Offline Reinforcement Learning Algorithm Customized for Multi-Task Fusion in Large-Scale Recommender Systems—0
Anomalous State Sequence Modeling to Enhance Safety in Reinforcement Learning—0
Anomaly Detection Under Controlled Sensing Using Actor-Critic Reinforcement Learning—0
A non-cooperative meta-modeling game for automated third-party calibrating, validating, and falsifying constitutive laws with parallelized adversarial attacks—0
An online evolving framework for advancing reinforcement-learning based automated vehicle control—0
An Online Model-Following Projection Mechanism Using Reinforcement Learning—0
An Online Prediction Algorithm for Reinforcement Learning with Linear Function Approximation using Cross Entropy Method—0
An open source Multi-Agent Deep Reinforcement Learning Routing Simulator for satellite networks—0
An Optics Controlling Environment and Reinforcement Learning Benchmarks—0
An Optimal Control View of Adversarial Machine Learning—0
An Optimal Online Method of Selecting Source Policies for Reinforcement Learning—0
An Optimization Framework for Task Sequencing in Curriculum Learning—0
An Optimization Method-Assisted Ensemble Deep Reinforcement Learning Algorithm to Solve Unit Commitment Problems—0
An Option-Dependent Analysis of Regret Minimization Algorithms in Finite-Horizon Semi-Markov Decision Processes—0
An Oracle and Observations for the OpenAI Gym / ALE Freeway Environment—0
Reinforcement Learning with Wasserstein Distance Regularisation, with Applications to Multipolicy Learning—0
A novel agent with formal goal-reaching guarantees: an experimental study with a mobile robot—0
A novel approach for multi-agent cooperative pursuit to capture grouped evaders—0
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Benchmark Results

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