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 4141–4150 of 15113 papers

TitleStatusHype
AdapShare: An RL-Based Dynamic Spectrum Sharing Solution for O-RAN—0
Adaptable Automation with Modular Deep Reinforcement Learning and Policy Transfer—0
Query Rewriting for Effective Misinformation Discovery—0
Adaptable image quality assessment using meta-reinforcement learning of task amenability—0
Adaptable Recovery Behaviors in Robotics: A Behavior Trees and Motion Generators(BTMG) Approach for Failure Management—0
Adaptation of Quadruped Robot Locomotion with Meta-Learning—0
AdapThink: Adaptive Thinking Preferences for Reasoning Language Model—0
Adapting Auxiliary Losses Using Gradient Similarity—0
Adapting Behaviour via Intrinsic Reward: A Survey and Empirical Study—0
Adapting Sampling Interval of Sensor Networks Using On-Line Reinforcement Learning—0
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Benchmark Results

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