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 4511–4520 of 15113 papers

TitleStatusHype
Benchmarking Feature Extractors for Reinforcement Learning-Based Semiconductor Defect Localization—0
Imagination-Augmented Hierarchical Reinforcement Learning for Safe and Interactive Autonomous Driving in Urban Environments—0
Data-Driven LQR using Reinforcement Learning and Quadratic Neural Networks—0
Runtime Verification of Learning Properties for Reinforcement Learning Algorithms—0
When Mining Electric Locomotives Meet Reinforcement Learning—0
Purpose in the Machine: Do Traffic Simulators Produce Distributionally Equivalent Outcomes for Reinforcement Learning Applications?—0
On-Policy Policy Gradient Reinforcement Learning Without On-Policy Sampling—0
Workflow-Guided Response Generation for Task-Oriented Dialogue—0
Adversarial Imitation Learning On Aggregated Data—0
An introduction to reinforcement learning for neuroscience—0
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Benchmark Results

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