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 1721–1730 of 15113 papers

TitleStatusHype
Full error analysis of policy gradient learning algorithms for exploratory linear quadratic mean-field control problem in continuous time with common noise—0
Active Sensing of Knee Osteoarthritis Progression with Reinforcement Learning—0
Reinforcement Learning for an Efficient and Effective Malware Investigation during Cyber Incident Response—0
Visual Grounding for Object-Level Generalization in Reinforcement LearningCode1
Coordinating Planning and Tracking in Layered Control Policies via Actor-Critic LearningCode0
TCR-GPT: Integrating Autoregressive Model and Reinforcement Learning for T-Cell Receptor Repertoires Generation—0
Multi-Objective Deep Reinforcement Learning for Optimisation in Autonomous Systems—0
A Survey on Self-play Methods in Reinforcement Learning—0
Adaptive Transit Signal Priority based on Deep Reinforcement Learning and Connected Vehicles in a Traffic Microsimulation Environment—0
On the Perturbed States for Transformed Input-robust Reinforcement LearningCode0
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Benchmark Results

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