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 8576–8600 of 15113 papers

TitleStatusHype
Reinforcement Learning for Intelligent Healthcare Systems: A Comprehensive Survey—0
Lyapunov Robust Constrained-MDPs: Soft-Constrained Robustly Stable Policy Optimization under Model Uncertainty—0
Online Model-Free Reinforcement Learning for the Automatic Control of a Flexible Wing Aircraft—0
RIS-assisted UAV Communications for IoT with Wireless Power Transfer Using Deep Reinforcement Learning—0
Distilling Neuron Spike with High Temperature in Reinforcement Learning Agents—0
Active Reinforcement Learning over MDPs—0
Ensemble Consensus-based Representation Deep Reinforcement Learning for Hybrid FSO/RF Communication Systems—0
DRL-based Slice Placement Under Non-Stationary Conditions—0
Beyond No Regret: Instance-Dependent PAC Reinforcement Learning—0
Deep Reinforcement Learning for Continuous Docking Control of Autonomous Underwater Vehicles: A Benchmarking Study—0
An Elementary Proof that Q-learning Converges Almost Surely—0
Learning Task Agnostic Skills with Data-driven GuidanceCode0
Parallelized Reverse Curriculum Generation—0
Policy Gradients Incorporating the Future—0
Offline Decentralized Multi-Agent Reinforcement Learning—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
Accelerating the Learning of TAMER with Counterfactual Explanations—0
Factor Representation and Decision Making in Stock Markets Using Deep Reinforcement Learning—0
Adversarial Attacks Against Deep Reinforcement Learning Framework in Internet of Vehicles—0
Interactive Reinforcement Learning for Table Balancing Robot—0
A survey of Monte Carlo methods for noisy and costly densities with application to reinforcement learning and ABC—0
Language-based General Action Template for Reinforcement Learning Agents—0
A Reinforcement Learning Approach for Scheduling in mmWave Networks—0
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Benchmark Results

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