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 4281–4290 of 15113 papers

TitleStatusHype
A Deep Reinforcement Learning-Based Controller for Magnetorheological-Damped Vehicle Suspension—0
A Deep Reinforcement Learning-based Adaptive Charging Policy for Wireless Rechargeable Sensor Networks—0
A Deep Reinforcement Learning-Based Caching Strategy for IoT Networks with Transient Data—0
A Deep Reinforcement Learning-Based Charging Scheduling Approach with Augmented Lagrangian for Electric Vehicle—0
Deep Reinforcement-Learning-based Driving Policy for Autonomous Road Vehicles—0
A Deep Reinforcement Learning-based Sliding Mode Control Design for Partially-known Nonlinear Systems—0
A Deep Reinforcement Learning Blind AI in DareFightingICE—0
A Deep Reinforcement Learning Chatbot—0
A Deep Reinforcement Learning Chatbot (Short Version)—0
A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systems—0
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Benchmark Results

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