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 5601–5625 of 15113 papers

TitleStatusHype
Inverse Reinforcement Learning with Locally Consistent Reward Functions—0
Inverse Reinforcement Learning with Missing Data—0
Inverse Reinforcement Learning with Multi-Relational Chains for Robot-Centered Smart Home—0
Inverse Reinforcement Learning with Multiple Ranked Experts—0
Inverse Reinforcement Learning with Natural Language Goals—0
Inverse Risk-Sensitive Reinforcement Learning—0
Investigating Enactive Learning for Autonomous Intelligent Agents—0
Investigating Gender Fairness in Machine Learning-driven Personalized Care for Chronic Pain—0
Investigating Generalisation in Continuous Deep Reinforcement Learning—0
Investigating Recurrence and Eligibility Traces in Deep Q-Networks—0
Investigating Reinforcement Learning Agents for Continuous State Space Environments—0
Investigating Robustness in Cyber-Physical Systems: Specification-Centric Analysis in the face of System Deviations—0
Investigating Simple Object Representations in Model-Free Deep Reinforcement Learning—0
Investigating the Edge of Stability Phenomenon in Reinforcement Learning—0
Investigating the Impact of Action Representations in Policy Gradient Algorithms—0
Investigating the Impact of Choice on Deep Reinforcement Learning for Space Controls—0
Investigating the Impact of Observation Space Design Choices On Training Reinforcement Learning Solutions for Spacecraft Problems—0
Investigating the Properties of Neural Network Representations in Reinforcement Learning—0
Investigating Value of Curriculum Reinforcement Learning in Autonomous Driving Under Diverse Road and Weather Conditions—0
Investigating Vulnerabilities of Deep Neural Policies—0
Investigation of Factorized Optical Flows as Mid-Level Representations—0
Investigation of Independent Reinforcement Learning Algorithms in Multi-Agent Environments—0
Investigation of Language Understanding Impact for Reinforcement Learning Based Dialogue Systems—0
Investigation of reinforcement learning for shape optimization of profile extrusion dies—0
Investigation of Sentiment Controllable Chatbot—0
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Benchmark Results

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