From Task Distributions to Expected Paths Lengths Distributions: Value Function Initialization in Sparse Reward Environments for Lifelong Reinforcement Learning
This paper studies value function transfer within reinforcement learning frameworks, focusing on tasks continuously assigned to an agent through a probabilistic distribution. Specifically, we focus on environments characterized by sparse rewards with a terminal goal. Initially, we propose and theore...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
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| Series: | Entropy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1099-4300/27/4/367 |
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