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 3441–3450 of 15113 papers

TitleStatusHype
Validation of massively-parallel adaptive testing using dynamic control matching—0
An Autonomous Non-monolithic Agent with Multi-mode Exploration based on Options FrameworkCode0
Online Portfolio Management via Deep Reinforcement Learning with High-Frequency DataCode1
A Transfer Learning Approach to Minimize Reinforcement Learning Risks in Energy Optimization for Smart Buildings—0
Joint Learning of Policy with Unknown Temporal Constraints for Safe Reinforcement Learning—0
X-RLflow: Graph Reinforcement Learning for Neural Network Subgraphs TransformationCode1
A Federated Reinforcement Learning Framework for Link Activation in Multi-link Wi-Fi Networks—0
One-Step Distributional Reinforcement Learning—0
Multi-criteria Hardware Trojan Detection: A Reinforcement Learning Approach—0
CROP: Towards Distributional-Shift Robust Reinforcement Learning using Compact Reshaped Observation ProcessingCode0
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Benchmark Results

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