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 3591–3600 of 15113 papers

TitleStatusHype
Cost-Sensitive Portfolio Selection via Deep Reinforcement Learning—0
Deep Reinforcement Learning for Constrained Field Development Optimization in Subsurface Two-phase Flow—0
Deep Reinforcement Learning for Contact-Rich Skills Using Compliant Movement Primitives—0
Deep Reinforcement Learning for Continuous Docking Control of Autonomous Underwater Vehicles: A Benchmarking Study—0
Automatic Representation for Lifetime Value Recommender Systems—0
Cost-Sensitive Exploration in Bayesian Reinforcement Learning—0
Deep Reinforcement Learning for Cyber Security—0
Automatic Risk Adaptation in Distributional Reinforcement Learning—0
A State Representation for Diminishing Rewards—0
CostNet: An End-to-End Framework for Goal-Directed Reinforcement Learning—0
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Benchmark Results

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