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 6276–6300 of 15113 papers

TitleStatusHype
A Deep Reinforcement Learning Strategy for UAV Autonomous Landing on a Platform—0
Concept-modulated model-based offline reinforcement learning for rapid generalization—0
A SUMO Framework for Deep Reinforcement Learning Experiments Solving Electric Vehicle Charging Dispatching Problem—0
Energy Optimization of Wind Turbines via a Neural Control Policy Based on Reinforcement Learning Markov Chain Monte Carlo Algorithm—0
Distilling Deep RL Models Into Interpretable Neuro-Fuzzy Systems—0
On the Near-Optimality of Local Policies in Large Cooperative Multi-Agent Reinforcement Learning—0
Project proposal: A modular reinforcement learning based automated theorem proverCode0
Annealing Optimization for Progressive Learning with Stochastic ApproximationCode0
Finite-Time Error Bounds for Greedy-GQ—0
Improving Assistive Robotics with Deep Reinforcement Learning—0
Reinforcement learning-based optimised control for tracking of nonlinear systems with adversarial attacks—0
Red Teaming with Mind Reading: White-Box Adversarial Policies Against RL AgentsCode0
SlateFree: a Model-Free Decomposition for Reinforcement Learning with Slate Actions—0
Natural Policy Gradients In Reinforcement Learning Explained—0
Prediction Based Decision Making for Autonomous Highway Driving—0
Variational Inference for Model-Free and Model-Based Reinforcement Learning—0
Model-Free Deep Reinforcement Learning in Software-Defined Networks—0
Statistical CSI-based Beamforming for RIS-Aided Multiuser MISO Systems using Deep Reinforcement Learning—0
TarGF: Learning Target Gradient Field to Rearrange Objects without Explicit Goal Specification—0
Taming Multi-Agent Reinforcement Learning with Estimator Variance Reduction—0
Learning Practical Communication Strategies in Cooperative Multi-Agent Reinforcement Learning—0
Dialogue Evaluation with Offline Reinforcement Learning—0
A Technique to Create Weaker Abstract Board Game Agents via Reinforcement Learning—0
Dynamics-Adaptive Continual Reinforcement Learning via Progressive Contextualization—0
Deep reinforcement learning for quantum multiparameter estimation—0
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Benchmark Results

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