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 4611–4620 of 15113 papers

TitleStatusHype
A Concise Introduction to Reinforcement Learning in Robotics—0
Harfang3D Dog-Fight Sandbox: A Reinforcement Learning Research Platform for the Customized Control Tasks of Fighter AircraftsCode2
Bootstrap Advantage Estimation for Policy Optimization in Reinforcement LearningCode0
Reinforcement Learning with Unbiased Policy Evaluation and Linear Function Approximation—0
Object-Category Aware Reinforcement Learning—0
Visual Reinforcement Learning with Self-Supervised 3D RepresentationsCode1
Personalized Federated Hypernetworks for Privacy Preservation in Multi-Task Reinforcement Learning—0
Towards Multi-Agent Reinforcement Learning driven Over-The-Counter Market Simulations—0
Policy Gradient With Serial Markov Chain Reasoning—0
Optimal Control of Material Micro-Structures—0
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Benchmark Results

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