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 3581–3590 of 15113 papers

TitleStatusHype
Muti-Agent Proximal Policy Optimization For Data Freshness in UAV-assisted Networks—0
Smoothed Q-learning—0
Real-Time Measurement-Driven Reinforcement Learning Control Approach for Uncertain Nonlinear Systems—0
Latent-Conditioned Policy Gradient for Multi-Objective Deep Reinforcement Learning—0
On the Benefits of Leveraging Structural Information in Planning Over the Learned Model—0
Replay Buffer with Local Forgetting for Adapting to Local Environment Changes in Deep Model-Based Reinforcement Learning—0
Adaptive Policy Learning for Offline-to-Online Reinforcement Learning—0
Act-Then-Measure: Reinforcement Learning for Partially Observable Environments with Active MeasuringCode0
Fast Rates for Maximum Entropy ExplorationCode0
Reinforcement Learning-based Wavefront Sensorless Adaptive Optics Approaches for Satellite-to-Ground Laser Communication—0
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Benchmark Results

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