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 4026–4050 of 15113 papers

TitleStatusHype
A Cognitive Architecture Based on a Learning Classifier System with Spiking Classifiers—0
A Collaborative Multi-agent Reinforcement Learning Framework for Dialog Action Decomposition—0
A note on stabilizing reinforcement learning—0
A Communication-Efficient Multi-Agent Actor-Critic Algorithm for Distributed Reinforcement Learning—0
A Comparative Analysis of Deep Reinforcement Learning-enabled Freeway Decision-making for Automated Vehicles—0
A Comparative Analysis of Expected and Distributional Reinforcement Learning—0
A Comparative Analysis of Machine Learning Techniques for IoT Intrusion Detection—0
A Comparative Analysis of Reinforcement Learning and Conventional Deep Learning Approaches for Bearing Fault Diagnosis—0
A comparative evaluation of machine learning methods for robot navigation through human crowds—0
A Comparative Study of AI-based Intrusion Detection Techniques in Critical Infrastructures—0
A Comparative Study of Deep Reinforcement Learning for Crop Production Management—0
A Comparative Study of Reinforcement Learning Techniques on Dialogue Management—0
A Comparison of Action Spaces for Learning Manipulation Tasks—0
A Comparison of Classical and Deep Reinforcement Learning Methods for HVAC Control—0
A comparison of controller architectures and learning mechanisms for arbitrary robot morphologies—0
A Comparison of learning algorithms on the Arcade Learning Environment—0
A Comparison of Prediction Algorithms and Nexting for Short Term Weather Forecasts—0
A Comparison of Reinforcement Learning Techniques for Fuzzy Cloud Auto-Scaling—0
A Comparison of Self-Play Algorithms Under a Generalized Framework—0
A Complementary Learning Systems Approach to Temporal Difference Learning—0
A comprehensive survey of research towards AI-enabled unmanned aerial systems in pre-, active-, and post-wildfire management—0
A Computational Framework for Motor Skill Acquisition—0
A Computational Model of Representation Learning in the Brain Cortex, Integrating Unsupervised and Reinforcement Learning—0
A Conceptual Framework for Externally-influenced Agents: An Assisted Reinforcement Learning Review—0
A Concise Introduction to Reinforcement Learning in Robotics—0
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Benchmark Results

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