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 2971–2980 of 15113 papers

TitleStatusHype
Guided Cooperation in Hierarchical Reinforcement Learning via Model-based RolloutCode0
Boosting Offline Reinforcement Learning for Autonomous Driving with Hierarchical Latent Skills—0
Reinforcement Learning for Robust Header Compression under Model Uncertainty—0
Offline to Online Learning for Real-Time Bandwidth Estimation—0
Limits of Actor-Critic Algorithms for Decision Tree Policies Learning in IBMDPs—0
KuaiSim: A Comprehensive Simulator for Recommender SystemsCode1
Robotic Offline RL from Internet Videos via Value-Function Pre-Training—0
H2O+: An Improved Framework for Hybrid Offline-and-Online RL with Dynamics Gaps—0
Curriculum Reinforcement Learning via Morphology-Environment Co-Evolution—0
Text2Reward: Reward Shaping with Language Models for Reinforcement LearningCode2
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Benchmark Results

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