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 3081–3090 of 15113 papers

TitleStatusHype
Collaborative Wideband Spectrum Sensing and Scheduling for Networked UAVs in UTM Systems—0
Actor-Critic with variable time discretization via sustained actions—0
Characterization of Human Balance through a Reinforcement Learning-based Muscle Controller—0
A Reinforcement Learning-Based Approach to Graph Discovery in D2D-Enabled Federated Learning—0
Exploiting Generalization in Offline Reinforcement Learning via Unseen State Augmentations—0
QDax: A Library for Quality-Diversity and Population-based Algorithms with Hardware Acceleration—0
Reinforcement Learning for Financial Index TrackingCode1
Nonprehensile Planar Manipulation through Reinforcement Learning with Multimodal Categorical Exploration—0
ESRL: Efficient Sampling-based Reinforcement Learning for Sequence GenerationCode1
PARL: A Unified Framework for Policy Alignment in Reinforcement Learning from Human Feedback—0
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Benchmark Results

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