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Bridging the Performance Gap Between Target-Free and Target-Based Reinforcement Learning
arXiv – CS AI|Th\'eo Vincent, Yogesh Tripathi, Tim Faust, Abdullah Akg\"ul, Yaniv Oren, Melih Kandemir, Jan Peters, Carlo D'Eramo||1 views
🤖AI Summary
Researchers introduce iterated Shared Q-Learning (iS-QL), a new reinforcement learning method that bridges target-free and target-based approaches by using only the last linear layer as a target network while sharing other parameters. The technique achieves comparable performance to traditional target-based methods while maintaining the memory efficiency of target-free approaches.
Key Takeaways
- →iS-QL uses only the last linear layer as a target network while sharing remaining parameters with the online network.
- →The method maintains low memory footprint of target-free approaches while leveraging benefits of target-based literature.
- →Combining with iterated Q-learning improves sample efficiency by learning consecutive Bellman updates in parallel.
- →The approach bridges performance gap between target-free and target-based reinforcement learning methods.
- →iS-QL represents a step toward more resource-efficient reinforcement learning algorithms.
#reinforcement-learning#deep-learning#q-learning#target-networks#memory-efficiency#algorithm-optimization#machine-learning#research
Read Original →via arXiv – CS AI
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