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 4331–4340 of 15113 papers

TitleStatusHype
Advancing RAN Slicing with Offline Reinforcement Learning—0
Advancing Renewable Electricity Consumption With Reinforcement Learning—0
Advancing Text-Driven Chest X-Ray Generation with Policy-Based Reinforcement Learning—0
Advantage Actor-Critic with Reasoner: Explaining the Agent's Behavior from an Exploratory Perspective—0
Advantage Amplification in Slowly Evolving Latent-State Environments—0
Advantages and Limitations of using Successor Features for Transfer in Reinforcement Learning—0
Advantage Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning—0
AdverSAR: Adversarial Search and Rescue via Multi-Agent Reinforcement Learning—0
Adversarial Attacks Against Deep Reinforcement Learning Framework in Internet of Vehicles—0
Adversarial Attacks and Detection on Reinforcement Learning-Based Interactive Recommender Systems—0
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Benchmark Results

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