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 3511–3520 of 15113 papers

TitleStatusHype
Enhancing Spectrum Efficiency in 6G Satellite Networks: A GAIL-Powered Policy Learning via Asynchronous Federated Inverse Reinforcement Learning—0
Autoregressive Multi-trait Essay Scoring via Reinforcement Learning with Scoring-aware Multiple Rewards—0
LoopSR: Looping Sim-and-Real for Lifelong Policy Adaptation of Legged Robots—0
Optimizing Downlink C-NOMA Transmission with Movable Antennas: A DDPG-based Approach—0
OffRIPP: Offline RL-based Informative Path Planning—0
Asynchronous Fractional Multi-Agent Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing—0
On-orbit Servicing for Spacecraft Collision Avoidance With Autonomous Decision Making—0
Learning with Dynamics: Autonomous Regulation of UAV Based Communication Networks with Dynamic UAV Crew—0
Spiders Based on Anxiety: How Reinforcement Learning Can Deliver Desired User Experience in Virtual Reality Personalized Arachnophobia TreatmentCode0
A random measure approach to reinforcement learning in continuous time—0
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Benchmark Results

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