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 3681–3690 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning for Optimal Control of Space Heating—0
Deep Reinforcement Learning for Optimal Critical Care Pain Management with Morphine using Dueling Double-Deep Q Networks—0
Deep Reinforcement Learning for Optimal Investment and Saving Strategy Selection in Heterogeneous Profiles: Intelligent Agents working towards retirement—0
Autonomous Overtaking in Gran Turismo Sport Using Curriculum Reinforcement Learning—0
Deep Reinforcement Learning for Optimal Power Flow with Renewables Using Graph Information—0
Deep reinforcement learning for optimal well control in subsurface systems with uncertain geology—0
Deep Reinforcement Learning for Optimizing RIS-Assisted HD-FD Wireless Systems—0
Deep Reinforcement Learning for Option Replication and Hedging—0
Deep Reinforcement Learning for Organ Localization in CT—0
A State Aggregation Approach for Solving Knapsack Problem with Deep Reinforcement Learning—0
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Benchmark Results

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