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 4021–4030 of 15113 papers

TitleStatusHype
Generalize by Touching: Tactile Ensemble Skill Transfer for Robotic Furniture Assembly—0
Enhancing Privacy and Security of Autonomous UAV Navigation—0
Knowledge Transfer for Cross-Domain Reinforcement Learning: A Systematic Review—0
EEG_RL-Net: Enhancing EEG MI Classification through Reinforcement Learning-Optimised Graph Neural Networks—0
Offline Reinforcement Learning with Behavioral Supervisor Tuning—0
Structured Reinforcement Learning for Delay-Optimal Data Transmission in Dense mmWave Networks—0
GRSN: Gated Recurrent Spiking Neurons for POMDPs and MARL—0
ActiveRIR: Active Audio-Visual Exploration for Acoustic Environment Modeling—0
DPO: A Differential and Pointwise Control Approach to Reinforcement Learning—0
An MRP Formulation for Supervised Learning: Generalized Temporal Difference Learning Models—0
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Benchmark Results

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