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

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Showing 7651–7675 of 15113 papers

TitleStatusHype
RIS-assisted UAV Communications for IoT with Wireless Power Transfer Using Deep Reinforcement Learning—0
The AI Economist: Optimal Economic Policy Design via Two-level Deep Reinforcement LearningCode1
Online Model-Free Reinforcement Learning for the Automatic Control of a Flexible Wing Aircraft—0
Lyapunov Robust Constrained-MDPs: Soft-Constrained Robustly Stable Policy Optimization under Model Uncertainty—0
On the Robustness of Controlled Deep Reinforcement Learning for Slice Placement—0
Mean-Field Multi-Agent Reinforcement Learning: A Decentralized Network Approach—0
Beyond No Regret: Instance-Dependent PAC Reinforcement Learning—0
DRL-based Slice Placement Under Non-Stationary Conditions—0
Ensemble Consensus-based Representation Deep Reinforcement Learning for Hybrid FSO/RF Communication Systems—0
Active Reinforcement Learning over MDPs—0
Deep Reinforcement Learning for Continuous Docking Control of Autonomous Underwater Vehicles: A Benchmarking Study—0
Learning Task Agnostic Skills with Data-driven GuidanceCode0
Learning Barrier Certificates: Towards Safe Reinforcement Learning with Zero Training-time ViolationsCode1
Offline Decentralized Multi-Agent Reinforcement Learning—0
Parallelized Reverse Curriculum Generation—0
Policy Gradients Incorporating the Future—0
Optimal Management of the Peak Power Penalty for Smart Grids Using MPC-based Reinforcement Learning—0
Deep Reinforcement Learning Based Networked Control with Network Delays for Signal Temporal Logic Specifications—0
Controlled Deep Reinforcement Learning for Optimized Slice Placement—0
Factor Representation and Decision Making in Stock Markets Using Deep Reinforcement Learning—0
Accelerating the Learning of TAMER with Counterfactual Explanations—0
Adversarial Attacks Against Deep Reinforcement Learning Framework in Internet of Vehicles—0
A Proposal: Interactively Learning to Summarise Timelines by Reinforcement Learning—0
Interactive Reinforcement Learning for Table Balancing Robot—0
Better Chinese Sentence Segmentation with Reinforcement Learning—0
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Benchmark Results

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