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 4401–4410 of 15113 papers

TitleStatusHype
Solving Collaborative Dec-POMDPs with Deep Reinforcement Learning Heuristics—0
Deep W-Networks: Solving Multi-Objective Optimisation Problems With Deep Reinforcement LearningCode0
Foundation Models for Semantic Novelty in Reinforcement Learning—0
Leveraging Sequentiality in Reinforcement Learning from a Single DemonstrationCode0
Leveraging Offline Data in Online Reinforcement Learning—0
Interpretable Deep Reinforcement Learning for Green Security Games with Real-Time Information—0
Doubly Inhomogeneous Reinforcement LearningCode0
Efficient Compressed Ratio Estimation Using Online Sequential Learning for Edge Computing—0
Learning to Follow Instructions in Text-Based GamesCode0
Pretraining in Deep Reinforcement Learning: A Survey—0
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Benchmark Results

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