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 4926–4950 of 15113 papers

TitleStatusHype
Distilling Deep RL Models Into Interpretable Neuro-Fuzzy Systems—0
A SUMO Framework for Deep Reinforcement Learning Experiments Solving Electric Vehicle Charging Dispatching Problem—0
DC-MRTA: Decentralized Multi-Robot Task Allocation and Navigation in Complex Environments—0
On the Near-Optimality of Local Policies in Large Cooperative Multi-Agent Reinforcement Learning—0
A Deep Reinforcement Learning Strategy for UAV Autonomous Landing on a Platform—0
Finite-Time Error Bounds for Greedy-GQ—0
Annealing Optimization for Progressive Learning with Stochastic ApproximationCode0
Project proposal: A modular reinforcement learning based automated theorem proverCode0
SlateFree: a Model-Free Decomposition for Reinforcement Learning with Slate Actions—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
Prediction Based Decision Making for Autonomous Highway Driving—0
Natural Policy Gradients In Reinforcement Learning Explained—0
Improving Assistive Robotics with Deep Reinforcement Learning—0
Variational Inference for Model-Free and Model-Based Reinforcement Learning—0
Statistical CSI-based Beamforming for RIS-Aided Multiuser MISO Systems using Deep Reinforcement Learning—0
Model-Free Deep Reinforcement Learning in Software-Defined Networks—0
Learning Practical Communication Strategies in Cooperative Multi-Agent Reinforcement Learning—0
Dialogue Evaluation with Offline 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
MetaTrader: An Reinforcement Learning Approach Integrating Diverse Policies for Portfolio Optimization—0
Systems Theoretic Process Analysis of a Run Time Assured Neural Network Control System—0
Transformers are Sample-Efficient World ModelsCode2
Actor Prioritized Experience ReplayCode1
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Benchmark Results

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