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
Online Symbolic Music Alignment with Offline Reinforcement LearningCode1
Laboratory Experiments of Model-based Reinforcement Learning for Adaptive Optics ControlCode0
Causal State Distillation for Explainable Reinforcement LearningCode0
Design Space Exploration of Approximate Computing Techniques with a Reinforcement Learning Approach—0
Resilient Constrained Reinforcement Learning—0
Rethinking Model-based, Policy-based, and Value-based Reinforcement Learning via the Lens of Representation ComplexityCode0
Generalizable Visual Reinforcement Learning with Segment Anything ModelCode1
Beyond PID Controllers: PPO with Neuralized PID Policy for Proton Beam Intensity Control in Mu2e—0
RL-MPCA: A Reinforcement Learning Based Multi-Phase Computation Allocation Approach for Recommender Systems—0
Conversational Question Answering with Reformulations over Knowledge Graph—0
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Benchmark Results

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