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 4031–4040 of 15113 papers

TitleStatusHype
Extreme Q-Learning: MaxEnt RL without EntropyCode1
Data-Driven Inverse Reinforcement Learning for Expert-Learner Zero-Sum Games—0
Learning-based MPC from Big Data Using Reinforcement Learning—0
Emergent collective intelligence from massive-agent cooperation and competitionCode1
Robofriend: An Adpative Storytelling Robotic Teddy Bear - Technical ReportCode0
UAV aided Metaverse over Wireless Communications: A Reinforcement Learning Approach—0
Towards Deployable RL - What's Broken with RL Research and a Potential Fix—0
Offline Evaluation for Reinforcement Learning-based Recommendation: A Critical Issue and Some Alternatives—0
Safe Reinforcement Learning for an Energy-Efficient Driver Assistance System—0
Temporal Difference Learning with Compressed Updates: Error-Feedback meets Reinforcement Learning—0
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Benchmark Results

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