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 4591–4600 of 15113 papers

TitleStatusHype
A Policy-Guided Imitation Approach for Offline Reinforcement LearningCode1
PI-QT-Opt: Predictive Information Improves Multi-Task Robotic Reinforcement Learning at Scale—0
Reinforcement Learning for ConnectX—0
When to Update Your Model: Constrained Model-based Reinforcement LearningCode1
Multi-trainer Interactive Reinforcement Learning System—0
WILD-SCAV: Benchmarking FPS Gaming AI on Unity3D-based EnvironmentsCode1
Skill-Based Reinforcement Learning with Intrinsic Reward MatchingCode1
Robust Preference Learning for Storytelling via Contrastive Reinforcement Learning—0
ToupleGDD: A Fine-Designed Solution of Influence Maximization by Deep Reinforcement LearningCode1
Model-based Safe Deep Reinforcement Learning via a Constrained Proximal Policy Optimization AlgorithmCode1
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Benchmark Results

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