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 2591–2600 of 15113 papers

TitleStatusHype
Autonomous Reinforcement of Behavioral Sequences in Neural Dynamics—0
Autonomous Reinforcement Learning of Multiple Interrelated Tasks—0
A note on stabilizing reinforcement learning—0
Autonomous Ramp Merge Maneuver Based on Reinforcement Learning with Continuous Action Space—0
A Maintenance Planning Framework using Online and Offline Deep Reinforcement Learning—0
Autonomous Radiotherapy Treatment Planning Using DOLA: A Privacy-Preserving, LLM-Based Optimization Agent—0
Adaptive operator selection utilising generalised experience—0
Cooperative-Competitive Reinforcement Learning with History-Dependent Rewards—0
Autonomous Quadrotor Landing using Deep Reinforcement Learning—0
Autonomous Platoon Control with Integrated Deep Reinforcement Learning and Dynamic Programming—0
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Benchmark Results

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